Detecting anomalous transactions within an application by privileged user accounts

ABSTRACT

In one embodiment, a device obtains data regarding a transaction attempted by a user account within an online application that is captured by instrumentation code that is inserted into the online application at runtime, wherein the user account has sufficient privileges within the online application to perform the transaction. The device makes an inference about the data regarding the transaction using a behavioral model. The device determines, based on the inference, a mitigation action for performance within the online application according to an enforcement policy. The device enforces the mitigation action within the online application.

TECHNICAL FIELD

The present disclosure relates generally to computer systems, and, moreparticularly, to detecting anomalous transactions within an applicationby privileged user accounts.

BACKGROUND

The Internet and the World Wide Web have enabled the proliferation ofweb services available for virtually all types of businesses. Due to theaccompanying complexity of the infrastructure supporting the webservices, it is becoming increasingly difficult to maintain the highestlevel of service performance and user experience to keep up with theincrease in web services. For example, it can be challenging to piecetogether monitoring and logging data across disparate systems, tools,and layers in a network architecture. Moreover, even when data can beobtained, it is difficult to directly connect the chain of events andcause and effect. To this end, various application performancemanagement (APM) solutions have emerged that typically rely oninstrumentation, which is the process of inserting code into anapplication, to capture performance data.

Performance monitoring using instrumentation may help ensure that agiven application serves its users within guaranteed, required, etc.performance bounds. So far, instrumentation that has been used for APMhas been implemented under the assumption that the given application hasits own built-in security mechanisms. However, built-in securitymechanisms tend to be binary in their operations: either a user accounthas sufficient privileges to perform a transaction or not. This meansthat an individual, while using the user account, could still abusetheir privileges within the application (e.g., by performing datamining/exfiltration actions, etc.).

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments herein may be better understood by referring to thefollowing description in conjunction with the accompanying drawings inwhich like reference numerals indicate identically or functionallysimilar elements, of which:

FIGS. 1A-1B illustrate an example computer network;

FIG. 2 illustrates an example computing device/node;

FIG. 3 illustrates an example application intelligence platform;

FIG. 4 illustrates an example system for implementing the exampleapplication intelligence platform;

FIG. 5 illustrates an example computing system implementing thedisclosed technology;

FIG. 6 illustrates an example simplified architecture for a multi-tenantagent;

FIGS. 7A-7E illustrate an example simplified architecture forinstrumenting applications to prevent abuse by privileged users;

FIG. 8 illustrates an example simplified agent/tenant for detectinganomalous transactions within an application by privileged useraccounts;

FIG. 9 illustrate an example insight and associated enforcement policy;and

FIG. 10 illustrates an example simplified procedure for detectinganomalous transactions within an application by privileged useraccounts, in accordance with one or more embodiments described herein.

DESCRIPTION OF EXAMPLE EMBODIMENTS Overview

According to one or more embodiments of the disclosure, a device obtainsdata regarding a transaction attempted by a user account within anonline application that is captured by instrumentation code that isinserted into the online application at runtime, wherein the useraccount has sufficient privileges within the online application toperform the transaction. The device makes an inference about the dataregarding the transaction using a behavioral model. The devicedetermines, based on the inference, a mitigation action for performancewithin the online application according to an enforcement policy. Thedevice enforces the mitigation action within the online application.

Other embodiments are described below, and this overview is not meant tolimit the scope of the present disclosure.

Description

A computer network is a geographically distributed collection of nodesinterconnected by communication links and segments for transporting databetween end nodes, such as personal computers and workstations, or otherdevices, such as sensors, etc. Many types of networks are available,ranging from local area networks (LANs) to wide area networks (WANs).LANs typically connect the nodes over dedicated private communicationslinks located in the same general physical location, such as a buildingor campus. WANs, on the other hand, typically connect geographicallydispersed nodes over long-distance communications links, such as commoncarrier telephone lines, optical lightpaths, synchronous opticalnetworks (SONET), synchronous digital hierarchy (SDH) links, orPowerline Communications (PLC), and others. The Internet is an exampleof a WAN that connects disparate networks throughout the world,providing global communication between nodes on various networks. Othertypes of networks, such as field area networks (FANs), neighborhood areanetworks (NANs), personal area networks (PANs), enterprise networks,etc. may also make up the components of any given computer network.

The nodes typically communicate over the network by exchanging discreteframes or packets of data according to predefined protocols, such as theTransmission Control Protocol/Internet Protocol (TCP/IP). In thiscontext, a protocol consists of a set of rules defining how the nodesinteract with each other. Computer networks may be furtherinterconnected by an intermediate network node, such as a router, toextend the effective “size” of each network.

Smart object networks, such as sensor networks, in particular, are aspecific type of network having spatially distributed autonomous devicessuch as sensors, actuators, etc., that cooperatively monitor physical orenvironmental conditions at different locations, such as, e.g.,energy/power consumption, resource consumption (e.g., water/gas/etc. foradvanced metering infrastructure or “AMI” applications) temperature,pressure, vibration, sound, radiation, motion, pollutants, etc. Othertypes of smart objects include actuators, e.g., responsible for turningon/off an engine or perform any other actions. Sensor networks, a typeof smart object network, are typically shared-media networks, such aswireless or power-line communication networks. That is, in addition toone or more sensors, each sensor device (node) in a sensor network maygenerally be equipped with a radio transceiver or other communicationport, a microcontroller, and an energy source, such as a battery.Generally, size and cost constraints on smart object nodes (e.g.,sensors) result in corresponding constraints on resources such asenergy, memory, computational speed and bandwidth.

FIG. 1A is a schematic block diagram of an example computer network 100illustratively comprising nodes/devices, such as a plurality ofrouters/devices interconnected by links or networks, as shown. Forexample, customer edge (CE) routers 110 may be interconnected withprovider edge (PE) routers 120 (e.g., PE-1, PE-2, and PE-3) in order tocommunicate across a core network, such as an illustrative networkbackbone 130. For example, routers 110, 120 may be interconnected by thepublic Internet, a multiprotocol label switching (MPLS) virtual privatenetwork (VPN), or the like. Data packets 140 (e.g., traffic/messages)may be exchanged among the nodes/devices of the computer network 100over links using predefined network communication protocols such as theTransmission Control Protocol/Internet Protocol (TCP/IP), User DatagramProtocol (UDP), Asynchronous Transfer Mode (ATM) protocol, Frame Relayprotocol, or any other suitable protocol. Those skilled in the art willunderstand that any number of nodes, devices, links, etc. may be used inthe computer network, and that the view shown herein is for simplicity.

In some implementations, a router or a set of routers may be connectedto a private network (e.g., dedicated leased lines, an optical network,etc.) or a virtual private network (VPN), such as an MPLS VPN thanks toa carrier network, via one or more links exhibiting very differentnetwork and service level agreement characteristics.

FIG. 1B illustrates an example of network 100 in greater detail,according to various embodiments. As shown, network backbone 130 mayprovide connectivity between devices located in different geographicalareas and/or different types of local networks. For example, network 100may comprise local/branch networks 160, 162 that include devices/nodes10-16 and devices/nodes 18-20, respectively, as well as a datacenter/cloud environment 150 that includes servers 152-154. Notably,local networks 160-162 and data center/cloud environment 150 may belocated in different geographic locations. Servers 152-154 may include,in various embodiments, any number of suitable servers or othercloud-based resources. As would be appreciated, network 100 may includeany number of local networks, data centers, cloud environments,devices/nodes, servers, etc.

In some embodiments, the techniques herein may be applied to othernetwork topologies and configurations. For example, the techniquesherein may be applied to peering points with high-speed links, datacenters, etc. Furthermore, in various embodiments, network 100 mayinclude one or more mesh networks, such as an Internet of Thingsnetwork. Loosely, the term “Internet of Things” or “IoT” refers touniquely identifiable objects (things) and their virtual representationsin a network-based architecture. In particular, the next frontier in theevolution of the Internet is the ability to connect more than justcomputers and communications devices, but rather the ability to connect“objects” in general, such as lights, appliances, vehicles, heating,ventilating, and air-conditioning (HVAC), windows and window shades andblinds, doors, locks, etc. The “Internet of Things” thus generallyrefers to the interconnection of objects (e.g., smart objects), such assensors and actuators, over a computer network (e.g., via IP), which maybe the public Internet or a private network.

Notably, shared-media mesh networks, such as wireless networks, areoften on what is referred to as Low-Power and Lossy Networks (LLNs),which are a class of network in which both the routers and theirinterconnect are constrained: LLN routers typically operate withconstraints, e.g., processing power, memory, and/or energy (battery),and their interconnects are characterized by, illustratively, high lossrates, low data rates, and/or instability. LLNs are comprised ofanything from a few dozen to thousands or even millions of LLN routers,and support point-to-point traffic (between devices inside the LLN),point-to-multipoint traffic (from a central control point such at theroot node to a subset of devices inside the LLN), andmultipoint-to-point traffic (from devices inside the LLN towards acentral control point). Often, an IoT network is implemented with anLLN-like architecture. For example, as shown, local network 160 may bean LLN in which CE-2 operates as a root node for nodes/devices 10-16 inthe local mesh, in some embodiments.

FIG. 2 is a schematic block diagram of an example computing device(e.g., apparatus) 200 that may be used with one or more embodimentsdescribed herein, e.g., as any of the devices shown in FIGS. 1A-1Babove, and particularly as specific devices as described further below.The device may comprise one or more network interfaces 210 (e.g., wired,wireless, etc.), at least one processor 220, and a memory 240interconnected by a system bus 250, as well as a power supply 260 (e.g.,battery, plug-in, etc.).

The network interface(s) 210 contain the mechanical, electrical, andsignaling circuitry for communicating data over links coupled to thenetwork 100, e.g., providing a data connection between device 200 andthe data network, such as the Internet. The network interfaces may beconfigured to transmit and/or receive data using a variety of differentcommunication protocols. For example, interfaces 210 may include wiredtransceivers, wireless transceivers, cellular transceivers, or the like,each to allow device 200 to communicate information to and from a remotecomputing device or server over an appropriate network. The same networkinterfaces 210 also allow communities of multiple devices 200 tointerconnect among themselves, either peer-to-peer, or up and down ahierarchy. Note, further, that the nodes may have two different types ofnetwork connections via network interface(s) 210, e.g., wireless andwired/physical connections, and that the view herein is merely forillustration. Also, while network interface(s) 210 are shown separatelyfrom power supply 260, for devices using powerline communication (PLC)or Power over Ethernet (PoE), the network interface 210 may communicatethrough the power supply 260, or may be an integral component of thepower supply.

The memory 240 comprises a plurality of storage locations that areaddressable by the processor 220 and the network interfaces 210 forstoring software programs and data structures associated with theembodiments described herein. The processor 220 may comprise hardwareelements or hardware logic adapted to execute the software programs andmanipulate the data structures 245. An operating system 242, portions ofwhich are typically resident in memory 240 and executed by theprocessor, functionally organizes the device by, among other things,invoking operations in support of software processes and/or servicesexecuting on the device. These software processes and/or services maycomprise one or more functional processes 246, and on certain devices,an illustrative monitoring process 248, as described herein. Notably,functional processes 246, when executed by processor(s) 220, cause eachparticular device 200 to perform the various functions corresponding tothe particular device's purpose and general configuration. For example,a router would be configured to operate as a router, a server would beconfigured to operate as a server, an access point (or gateway) would beconfigured to operate as an access point (or gateway), a client devicewould be configured to operate as a client device, and so on.

It will be apparent to those skilled in the art that other processor andmemory types, including various computer-readable media, may be used tostore and execute program instructions pertaining to the techniquesdescribed herein. Also, while the description illustrates variousprocesses, it is expressly contemplated that various processes may beembodied as modules configured to operate in accordance with thetechniques herein (e.g., according to the functionality of a similarprocess). Further, while the processes have been shown separately, thoseskilled in the art will appreciate that processes may be routines ormodules within other processes.

Application Intelligence Platform

The embodiments herein relate to an application intelligence platformfor application performance management. In one aspect, as discussed withrespect to FIGS. 3-5 below, performance within a networking environmentmay be monitored, specifically by monitoring applications and entities(e.g., transactions, tiers, nodes, and machines) in the networkingenvironment using agents installed at individual machines at theentities. As an example, applications may be configured to run on one ormore machines (e.g., a customer will typically run one or more nodes ona machine, where an application consists of one or more tiers, and atier consists of one or more nodes). The agents collect data associatedwith the applications of interest and associated nodes and machineswhere the applications are being operated. Examples of the collecteddata may include performance data (e.g., metrics, metadata, etc.) andtopology data (e.g., indicating relationship information). Theagent-collected data may then be provided to one or more servers orcontrollers to analyze the data.

FIG. 3 is a block diagram of an example application intelligenceplatform 300 that can implement one or more aspects of the techniquesherein. The application intelligence platform is a system that monitorsand collects metrics of performance data for an application environmentbeing monitored. At the simplest structure, the application intelligenceplatform includes one or more agents 310 and one or moreservers/controllers 320. Note that while FIG. 3 shows four agents (e.g.,Agent 1 through Agent 4) communicatively linked to a single controller,the total number of agents and controllers can vary based on a number offactors including the number of applications monitored, how distributedthe application environment is, the level of monitoring desired, thelevel of user experience desired, and so on.

The controller 320 is the central processing and administration serverfor the application intelligence platform. The controller 320 serves abrowser-based user interface (UI) 330 that is the primary interface formonitoring, analyzing, and troubleshooting the monitored environment.The controller 320 can control and manage monitoring of businesstransactions (described below) distributed over application servers.Specifically, the controller 320 can receive runtime data from agents310 (and/or other coordinator devices), associate portions of businesstransaction data, communicate with agents to configure collection ofruntime data, and provide performance data and reporting through theinterface 330. The interface 330 may be viewed as a web-based interfaceviewable by a client device 340. In some implementations, a clientdevice 340 can directly communicate with controller 320 to view aninterface for monitoring data. The controller 320 can include avisualization system 350 for displaying the reports and dashboardsrelated to the disclosed technology. In some implementations, thevisualization system 350 can be implemented in a separate machine (e.g.,a server) different from the one hosting the controller 320.

Notably, in an illustrative Software as a Service (SaaS) implementation,a controller instance may be hosted remotely by a provider of theapplication intelligence platform 300. In an illustrative on-premises(On-Prem) implementation, a controller instance may be installed locallyand self-administered.

The controllers 320 receive data from different agents 310 (e.g., Agents1-4) deployed to monitor applications, databases and database servers,servers, and end user clients for the monitored environment. Any of theagents 310 can be implemented as different types of agents with specificmonitoring duties. For example, application agents may be installed oneach server that hosts applications to be monitored. Instrumenting anagent adds an application agent into the runtime process of theapplication.

Database agents, for example, may be software (e.g., a Java program)installed on a machine that has network access to the monitoreddatabases and the controller. Database agents query the monitoreddatabases in order to collect metrics and pass those metrics along fordisplay in a metric browser (e.g., for database monitoring and analysiswithin databases pages of the controller's UI 330). Multiple databaseagents can report to the same controller. Additional database agents canbe implemented as backup database agents to take over for the primarydatabase agents during a failure or planned machine downtime. Theadditional database agents can run on the same machine as the primaryagents or on different machines. A database agent can be deployed ineach distinct network of the monitored environment. Multiple databaseagents can run under different user accounts on the same machine.

Standalone machine agents, on the other hand, may be standalone programs(e.g., standalone Java programs) that collect hardware-relatedperformance statistics from the servers (or other suitable devices) inthe monitored environment. The standalone machine agents can be deployedon machines that host application servers, database servers, messagingservers, Web servers, etc. A standalone machine agent has an extensiblearchitecture (e.g., designed to accommodate changes).

End user monitoring (EUM) may be performed using browser agents andmobile agents to provide performance information from the point of viewof the client, such as a web browser or a mobile native application.Through EUM, web use, mobile use, or combinations thereof (e.g., by realusers or synthetic agents) can be monitored based on the monitoringneeds. Notably, browser agents (e.g., agents 310) can include Reportersthat report monitored data to the controller.

Monitoring through browser agents and mobile agents are generally unlikemonitoring through application agents, database agents, and standalonemachine agents that are on the server. In particular, browser agents maygenerally be embodied as small files using web-based technologies, suchas JavaScript agents injected into each instrumented web page (e.g., asclose to the top as possible) as the web page is served, and areconfigured to collect data. Once the web page has completed loading, thecollected data may be bundled into a beacon and sent to an EUMprocess/cloud for processing and made ready for retrieval by thecontroller. Browser real user monitoring (Browser RUM) provides insightsinto the performance of a web application from the point of view of areal or synthetic end user. For example, Browser RUM can determine howspecific Ajax or iframe calls are slowing down page load time and howserver performance impact end user experience in aggregate or inindividual cases.

A mobile agent, on the other hand, may be a small piece of highlyperformant code that gets added to the source of the mobile application.Mobile RUM provides information on the native mobile application (e.g.,iOS or Android applications) as the end users actually use the mobileapplication. Mobile RUM provides visibility into the functioning of themobile application itself and the mobile application's interaction withthe network used and any server-side applications with which the mobileapplication communicates.

Application Intelligence Monitoring: The disclosed technology canprovide application intelligence data by monitoring an applicationenvironment that includes various services such as web applicationsserved from an application server (e.g., Java virtual machine (JVM),Internet Information Services (IIS), Hypertext Preprocessor (PHP) Webserver, etc.), databases or other data stores, and remote services suchas message queues and caches. The services in the applicationenvironment can interact in various ways to provide a set of cohesiveuser interactions with the application, such as a set of user servicesapplicable to end user customers.

Application Intelligence Modeling: Entities in the applicationenvironment (such as the JBoss service, MQSeries modules, and databases)and the services provided by the entities (such as a login transaction,service or product search, or purchase transaction) may be mapped to anapplication intelligence model. In the application intelligence model, abusiness transaction represents a particular service provided by themonitored environment. For example, in an e-commerce application,particular real-world services can include a user logging in, searchingfor items, or adding items to the cart. In a content portal, particularreal-world services can include user requests for content such assports, business, or entertainment news. In a stock trading application,particular real-world services can include operations such as receivinga stock quote, buying, or selling stocks.

Business Transactions: A business transaction representation of theparticular service provided by the monitored environment provides a viewon performance data in the context of the various tiers that participatein processing a particular request. A business transaction, which mayeach be identified by a unique business transaction identification (ID),represents the end-to-end processing path used to fulfill a servicerequest in the monitored environment (e.g., adding items to a shoppingcart, storing information in a database, purchasing an item online,etc.). Thus, a business transaction is a type of user-initiated actionin the monitored environment defined by an entry point and a processingpath across application servers, databases, and potentially many otherinfrastructure components. Each instance of a business transaction is anexecution of that transaction in response to a particular user request(e.g., a socket call, illustratively associated with the TCP layer). Abusiness transaction can be created by detecting incoming requests at anentry point and tracking the activity associated with request at theoriginating tier and across distributed components in the applicationenvironment (e.g., associating the business transaction with a 4-tupleof a source IP address, source port, destination IP address, anddestination port). A flow map can be generated for a businesstransaction that shows the touch points for the business transaction inthe application environment. In one embodiment, a specific tag may beadded to packets by application specific agents for identifying businesstransactions (e.g., a custom header field attached to a hypertexttransfer protocol (HTTP) payload by an application agent, or by anetwork agent when an application makes a remote socket call), such thatpackets can be examined by network agents to identify the businesstransaction identifier (ID) (e.g., a Globally Unique Identifier (GUID)or Universally Unique Identifier (UUID)).

Performance monitoring can be oriented by business transaction to focuson the performance of the services in the application environment fromthe perspective of end users. Performance monitoring based on businesstransactions can provide information on whether a service is available(e.g., users can log in, check out, or view their data), response timesfor users, and the cause of problems when the problems occur.

A business application is the top-level container in the applicationintelligence model. A business application contains a set of relatedservices and business transactions. In some implementations, a singlebusiness application may be needed to model the environment. In someimplementations, the application intelligence model of the applicationenvironment can be divided into several business applications. Businessapplications can be organized differently based on the specifics of theapplication environment. One consideration is to organize the businessapplications in a way that reflects work teams in a particularorganization, since role-based access controls in the Controller UI areoriented by business application.

A node in the application intelligence model corresponds to a monitoredserver or JVM in the application environment. A node is the smallestunit of the modeled environment. In general, a node corresponds to anindividual application server, JVM, or Common Language Runtime (CLR) onwhich a monitoring Agent is installed. Each node identifies itself inthe application intelligence model. The Agent installed at the node isconfigured to specify the name of the node, tier, and businessapplication under which the Agent reports data to the Controller.

Business applications contain tiers, the unit in the applicationintelligence model that includes one or more nodes. Each node representsan instrumented service (such as a web application). While a node can bea distinct application in the application environment, in theapplication intelligence model, a node is a member of a tier, which,along with possibly many other tiers, make up the overall logicalbusiness application.

Tiers can be organized in the application intelligence model dependingon a mental model of the monitored application environment. For example,identical nodes can be grouped into a single tier (such as a cluster ofredundant servers). In some implementations, any set of nodes, identicalor not, can be grouped for the purpose of treating certain performancemetrics as a unit into a single tier.

The traffic in a business application flows among tiers and can bevisualized in a flow map using lines among tiers. In addition, the linesindicating the traffic flows among tiers can be annotated withperformance metrics. In the application intelligence model, there maynot be any interaction among nodes within a single tier. Also, in someimplementations, an application agent node cannot belong to more thanone tier. Similarly, a machine agent cannot belong to more than onetier. However, more than one machine agent can be installed on amachine.

A backend is a component that participates in the processing of abusiness transaction instance. A backend is not instrumented by anagent. A backend may be a web server, database, message queue, or othertype of service. The agent recognizes calls to these backend servicesfrom instrumented code (called exit calls). When a service is notinstrumented and cannot continue the transaction context of the call,the agent determines that the service is a backend component. The agentpicks up the transaction context at the response at the backend andcontinues to follow the context of the transaction from there.

Performance information is available for the backend call. For detailedtransaction analysis for the leg of a transaction processed by thebackend, the database, web service, or other application need to beinstrumented.

The application intelligence platform uses both self-learned baselinesand configurable thresholds to help identify application issues. Acomplex distributed application has a large number of performancemetrics and each metric is important in one or more contexts. In suchenvironments, it is difficult to determine the values or ranges that arenormal for a particular metric; set meaningful thresholds on which tobase and receive relevant alerts; and determine what is a “normal”metric when the application or infrastructure undergoes change. Forthese reasons, the disclosed application intelligence platform canperform anomaly detection based on dynamic baselines or thresholds.

The disclosed application intelligence platform automatically calculatesdynamic baselines for the monitored metrics, defining what is “normal”for each metric based on actual usage. The application intelligenceplatform uses these baselines to identify subsequent metrics whosevalues fall out of this normal range. Static thresholds that are tediousto set up and, in rapidly changing application environments,error-prone, are no longer needed.

The disclosed application intelligence platform can use configurablethresholds to maintain service level agreements (SLAs) and ensureoptimum performance levels for system by detecting slow, very slow, andstalled transactions. Configurable thresholds provide a flexible way toassociate the right business context with a slow request to isolate theroot cause.

In addition, health rules can be set up with conditions that use thedynamically generated baselines to trigger alerts or initiate othertypes of remedial actions when performance problems are occurring or maybe about to occur.

For example, dynamic baselines can be used to automatically establishwhat is considered normal behavior for a particular application.Policies and health rules can be used against baselines or other healthindicators for a particular application to detect and troubleshootproblems before users are affected. Health rules can be used to definemetric conditions to monitor, such as when the “average response time isfour times slower than the baseline.” The health rules can be createdand modified based on the monitored application environment.

Examples of health rules for testing business transaction performancecan include business transaction response time and business transactionerror rate. For example, health rule that tests whether the businesstransaction response time is much higher than normal can define acritical condition as the combination of an average response timegreater than the default baseline by 3 standard deviations and a loadgreater than 50 calls per minute. In some implementations, this healthrule can define a warning condition as the combination of an averageresponse time greater than the default baseline by 2 standard deviationsand a load greater than 100 calls per minute. In some implementations,the health rule that tests whether the business transaction error rateis much higher than normal can define a critical condition as thecombination of an error rate greater than the default baseline by 3standard deviations and an error rate greater than 10 errors per minuteand a load greater than 50 calls per minute. In some implementations,this health rule can define a warning condition as the combination of anerror rate greater than the default baseline by 2 standard deviationsand an error rate greater than 5 errors per minute and a load greaterthan 50 calls per minute. These are non-exhaustive and non-limitingexamples of health rules and other health rules can be defined asdesired by the user.

Policies can be configured to trigger actions when a health rule isviolated or when any event occurs. Triggered actions can includenotifications, diagnostic actions, auto-scaling capacity, runningremediation scripts.

Most of the metrics relate to the overall performance of the applicationor business transaction (e.g., load, average response time, error rate,etc.) or of the application server infrastructure (e.g., percentage CPUbusy, percentage of memory used, etc.). The Metric Browser in thecontroller UI can be used to view all of the metrics that the agentsreport to the controller.

In addition, special metrics called information points can be created toreport on how a given business (as opposed to a given application) isperforming. For example, the performance of the total revenue for acertain product or set of products can be monitored. Also, informationpoints can be used to report on how a given code is performing, forexample how many times a specific method is called and how long it istaking to execute. Moreover, extensions that use the machine agent canbe created to report user defined custom metrics. These custom metricsare base-lined and reported in the controller, just like the built-inmetrics.

All metrics can be accessed programmatically using a RepresentationalState Transfer (REST) API that returns either the JavaScript ObjectNotation (JSON) or the eXtensible Markup Language (XML) format. Also,the REST API can be used to query and manipulate the applicationenvironment.

Snapshots provide a detailed picture of a given application at a certainpoint in time. Snapshots usually include call graphs that allow thatenables drilling down to the line of code that may be causingperformance problems. The most common snapshots are transactionsnapshots.

FIG. 4 illustrates an example application intelligence platform (system)400 for performing one or more aspects of the techniques herein. Thesystem 400 in FIG. 4 includes client 405, client device 492, mobiledevice 415, network 420, network server 425, application servers 430,440, 450, and 460, asynchronous network machine 470, data stores 480 and485, controller 490, and data collection server 495. The controller 490can include visualization system 496 for providing displaying of thereport generated for performing the field name recommendations for fieldextraction as disclosed in the present disclosure. In someimplementations, the visualization system 496 can be implemented in aseparate machine (e.g., a server) different from the one hosting thecontroller 490.

Client 405 may include network browser 410 and be implemented as acomputing device, such as for example a laptop, desktop, workstation, orsome other computing device. Network browser 410 may be a clientapplication for viewing content provided by an application server, suchas application server 430 via network server 425 over network 420.

Network browser 410 may include agent 412. Agent 412 may be installed onnetwork browser 410 and/or client 405 as a network browser add-on,downloading the application to the server, or in some other manner.Agent 412 may be executed to monitor network browser 410, the operatingsystem of client 405, and any other application, API, or anothercomponent of client 405. Agent 412 may determine network browsernavigation timing metrics, access browser cookies, monitor code, andtransmit data to data collection server 495, controller 490, or anotherdevice. Agent 412 may perform other operations related to monitoring arequest or a network at client 405 as discussed herein including reportgenerating.

Mobile device 415 is connected to network 420 and may be implemented asa portable device suitable for sending and receiving content over anetwork, such as for example a mobile phone, smart phone, tabletcomputer, or other portable device. Both client 405 and mobile device415 may include hardware and/or software configured to access a webservice provided by network server 425.

Mobile device 415 may include network browser 417 and an agent 419.Mobile device may also include client applications and other code thatmay be monitored by agent 419. Agent 419 may reside in and/orcommunicate with network browser 417, as well as communicate with otherapplications, an operating system, APIs and other hardware and softwareon mobile device 415. Agent 419 may have similar functionality as thatdescribed herein for agent 412 on client 405, and may report data todata collection server 495 and/or controller 490.

Network 420 may facilitate communication of data among differentservers, devices and machines of system 400 (some connections shown withlines to network 420, some not shown). The network may be implemented asa private network, public network, intranet, the Internet, a cellularnetwork, Wi-Fi network, VoIP network, or a combination of one or more ofthese networks. The network 420 may include one or more machines such asload balance machines and other machines.

Network server 425 is connected to network 420 and may receive andprocess requests received over network 420. Network server 425 may beimplemented as one or more servers implementing a network service, andmay be implemented on the same machine as application server 430 or oneor more separate machines. When network 420 is the Internet, networkserver 425 may be implemented as a web server.

Application server 430 communicates with network server 425, applicationservers 440 and 450, and controller 490. Application server 450 may alsocommunicate with other machines and devices (not illustrated in FIG. 4). Application server 430 may host an application or portions of adistributed application. The host application 432 may be in one of manyplatforms, such as including a Java, PHP, .Net, and Node.JS, beimplemented as a Java virtual machine, or include some other host type.Application server 430 may also include one or more agents 434 (i.e.,“modules”), including a language agent, machine agent, and networkagent, and other software modules. Application server 430 may beimplemented as one server or multiple servers as illustrated in FIG. 4 .

Application 432 and other software on application server 430 may beinstrumented using byte code insertion, or byte code instrumentation(BCI), to modify the object code of the application or other software.The instrumented object code may include code used to detect callsreceived by application 432, calls sent by application 432, andcommunicate with agent 434 during execution of the application. BCI mayalso be used to monitor one or more sockets of the application and/orapplication server in order to monitor the socket and capture packetscoming over the socket.

In some embodiments, server 430 may include applications and/or codeother than a virtual machine. For example, servers 430, 440, 450, and460 may each include Java code, .Net code, PHP code, Ruby code, C code,C++ or other binary code to implement applications and process requestsreceived from a remote source. References to a virtual machine withrespect to an application server are intended to be for exemplarypurposes only.

Agents 434 on application server 430 may be installed, downloaded,embedded, or otherwise provided on application server 430. For example,agents 434 may be provided in server 430 by instrumentation of objectcode, downloading the agents to the server, or in some other manner.Agent 434 may be executed to monitor application server 430, monitorapplication 432 running in a virtual machine (or other program language,such as a PHP, .Net, or C program), machine resources, network layerdata, and communicate with byte instrumented code on application server430 and one or more applications on application server 430.

Each of agents 434, 444, 454, and 464 may include one or more agents,such as language agents, machine agents, and network agents. A languageagent may be a type of agent that is suitable to run on a particularhost. Examples of language agents include a Java agent, .Net agent, PHPagent, and other agents. The machine agent may collect data from aparticular machine on which it is installed. A network agent may capturenetwork information, such as data collected from a socket.

Agent 434 may detect operations such as receiving calls and sendingrequests by application server 430, resource usage, and incomingpackets. Agent 434 may receive data, process the data, for example byaggregating data into metrics, and transmit the data and/or metrics tocontroller 490. Agent 434 may perform other operations related tomonitoring applications and application server 430 as discussed herein.For example, agent 434 may identify other applications, share businesstransaction data, aggregate detected runtime data, and other operations.

An agent may operate to monitor a node, tier of nodes, or other entity.A node may be a software program or a hardware component (e.g., memory,processor, and so on). A tier of nodes may include a plurality of nodeswhich may process a similar business transaction, may be located on thesame server, may be associated with each other in some other way, or maynot be associated with each other.

A language agent may be an agent suitable to instrument or modify,collect data from, and reside on a host. The host may be a Java, PHP,.Net, Node.JS, or other type of platform. Language agents may collectflow data as well as data associated with the execution of a particularapplication. The language agent may instrument the lowest level of theapplication to gather the flow data. The flow data may indicate whichtier is communicating with which tier and on which port. In someinstances, the flow data collected from the language agent includes asource IP, a source port, a destination IP, and a destination port. Thelanguage agent may report the application data and call chain data to acontroller. The language agent may report the collected flow dataassociated with a particular application to a network agent.

A network agent may be a standalone agent that resides on the host andcollects network flow group data. The network flow group data mayinclude a source IP, destination port, destination IP, and protocolinformation for network flow received by an application on which networkagent is installed. The network agent may collect data by interceptingand performing packet capture on packets coming in from one or morenetwork interfaces (e.g., so that data generated/received by all theapplications using sockets can be intercepted). The network agent mayreceive flow data from a language agent that is associated withapplications to be monitored. For flows in the flow group data thatmatch flow data provided by the language agent, the network agent rollsup the flow data to determine metrics such as TCP throughput, TCP loss,latency, and bandwidth. The network agent may then report the metrics,flow group data, and call chain data to a controller. The network agentmay also make system calls at an application server to determine systeminformation, such as for example a host status check, a network statuscheck, socket status, and other information.

A machine agent, which may be referred to as an infrastructure agent,may reside on the host and collect information regarding the machinewhich implements the host. A machine agent may collect and generatemetrics from information such as processor usage, memory usage, andother hardware information.

Each of the language agent, network agent, and machine agent may reportdata to the controller. Controller 490 may be implemented as a remoteserver that communicates with agents located on one or more servers ormachines. The controller may receive metrics, call chain data and otherdata, correlate the received data as part of a distributed transaction,and report the correlated data in the context of a distributedapplication implemented by one or more monitored applications andoccurring over one or more monitored networks. The controller mayprovide reports, one or more user interfaces, and other information fora user.

Agent 434 may create a request identifier for a request received byserver 430 (for example, a request received by a client 405 or mobiledevice 415 associated with a user or another source). The requestidentifier may be sent to client 405 or mobile device 415, whicheverdevice sent the request. In embodiments, the request identifier may becreated when data is collected and analyzed for a particular businesstransaction.

Each of application servers 440, 450, and 460 may include an applicationand agents. Each application may run on the corresponding applicationserver. Each of applications 442, 452, and 462 on application servers440-460 may operate similarly to application 432 and perform at least aportion of a distributed business transaction. Agents 444, 454, and 464may monitor applications 442-462, collect and process data at runtime,and communicate with controller 490. The applications 432, 442, 452, and462 may communicate with each other as part of performing a distributedtransaction. Each application may call any application or method ofanother virtual machine.

Asynchronous network machine 470 may engage in asynchronouscommunications with one or more application servers, such as applicationserver 450 and 460. For example, application server 450 may transmitseveral calls or messages to an asynchronous network machine. Ratherthan communicate back to application server 450, the asynchronousnetwork machine may process the messages and eventually provide aresponse, such as a processed message, to application server 460.Because there is no return message from the asynchronous network machineto application server 450, the communications among them areasynchronous.

Data stores 480 and 485 may each be accessed by application servers suchas application server 460. Data store 485 may also be accessed byapplication server 450. Each of data stores 480 and 485 may store data,process data, and return queries received from an application server.Each of data stores 480 and 485 may or may not include an agent.

Controller 490 may control and manage monitoring of businesstransactions distributed over application servers 430-460. In someembodiments, controller 490 may receive application data, including dataassociated with monitoring client requests at client 405 and mobiledevice 415, from data collection server 495. In some embodiments,controller 490 may receive application monitoring data and network datafrom each of agents 412, 419, 434, 444, and 454 (also referred to hereinas “application monitoring agents”). Controller 490 may associateportions of business transaction data, communicate with agents toconfigure collection of data, and provide performance data and reportingthrough an interface. The interface may be viewed as a web-basedinterface viewable by client device 492, which may be a mobile device,client device, or any other platform for viewing an interface providedby controller 490. In some embodiments, a client device 492 may directlycommunicate with controller 490 to view an interface for monitoringdata.

Client device 492 may include any computing device, including a mobiledevice or a client computer such as a desktop, work station or othercomputing device. Client device 492 may communicate with controller 490to create and view a custom interface. In some embodiments, controller490 provides an interface for creating and viewing the custom interfaceas a content page, e.g., a web page, which may be provided to andrendered through a network browser application on client device 492.

Applications 432, 442, 452, and 462 may be any of several types ofapplications. Examples of applications that may implement applications432-462 include a Java, PHP, .Net, Node.JS, and other applications.

FIG. 5 is a block diagram of a computer system 500 for implementing thepresent technology, which is a specific implementation of device 200 ofFIG. 2 above. System 500 of FIG. 5 may be implemented in the contexts ofthe likes of client 405, client device 492, network server 425, servers430, 440, 450, 460, asynchronous network machine 470, and controller 490of FIG. 4 . (Note that the specifically configured system 500 of FIG. 5and the customized device 200 of FIG. 2 are not meant to be mutuallyexclusive, and the techniques herein may be performed by any suitablyconfigured computing device.)

The computing system 500 of FIG. 5 includes one or more processor(s) 510and memory 520. Main memory 520 stores, in part, instructions and datafor execution by processor(s) 510. Main memory 520 can store theexecutable code when in operation. The system 500 of FIG. 5 furtherincludes a mass storage device 530, portable/remote storage(s) 540,output devices 550, user input devices 560, display system(s) 570, andperipheral(s) 580.

The components shown in FIG. 5 are depicted as being connected via asingle bus 590. However, the components may be connected through one ormore data transport means. For example, processor(s) 510 and main memory520 may be connected via a local microprocessor bus, and the massstorage device 530, peripheral(s) 580, storage(s) 540, and displaysystem(s) 570 may be connected via one or more input/output (I/O) buses.

Mass storage device 530, which may be implemented with a magnetic diskdrive or an optical disk drive, is a non-volatile storage device forstoring data and instructions for use by processor(s) 510. Mass storagedevice 530 can store the system software for implementing embodiments ofthe present disclosure for purposes of loading that software into mainmemory 520.

Portable/remote storage(s) 540 may operate in conjunction with aportable non-volatile storage medium, such as a compact disk, digitalvideo disk, magnetic disk, flash storage, etc. to input and output dataand code to and from the computer system 500 of FIG. 5 . The systemsoftware for implementing embodiments of the present disclosure may bestored on such a portable medium and input to the computer system 500via the storage(s) 540.

Input devices 560 provide a portion of a user interface. Input devices560 may include an alpha-numeric keypad, such as a keyboard, forinputting alpha-numeric and other information, or a pointing device,such as a mouse, a trackball, stylus, or cursor direction keys.Additionally, the system 500 as shown in FIG. 5 includes output devices550. Examples of suitable output devices include speakers, printers,network interfaces, and monitors.

Display system(s) 570 may include a liquid crystal display (LCD) orother suitable display device. Display system(s) 570 receives textualand graphical information, and processes the information for output tothe display device.

Peripheral(s) 580 may include any type of computer support device to addadditional functionality to the computer system. For example,peripheral(s) 580 may include a modem or a router.

The components contained in the computer system 500 of FIG. 5 caninclude a personal computer, hand-held computing device, telephone,mobile computing device, workstation, server, minicomputer, mainframecomputer, or any other computing device. The computer can also includedifferent bus configurations, networked platforms, multi-processorplatforms, etc. Various operating systems can be used including Unix,Linux, Windows, Apple OS, and other suitable operating systems,including mobile versions.

When implementing a mobile device such as smart phone or tabletcomputer, the computer system 500 of FIG. 5 may include one or moreantennas, radios, and other circuitry for communicating over wirelesssignals, such as for example communication using Wi-Fi, cellular, orother wireless signals.

As noted above, a Java agent can be used for purposes of instrumenting aJava application. In general, a Java agent takes the form of a Javaclass that implements a premain method. Similar to the main method in aJava application, the premain method acts as an entry point for theagent. When the Java Virtual Machine (JVM) initializes, the premainmethod is called before calling the main method of the Java application.The Java agent may also include an agentmain method that can be used,after startup of the JVM. This allows the Java agent to be loaded eitherin a static manner (e.g., using premain as part of the JVMinitialization) or in a dynamic manner, such as by using the Java attachAPI to call the agentmain method of the agent while the JVM is alreadyrunning.

Associated with a Java agent may be a manifest that specifies a set ofattributes for the agent, as follows:

TABLE 1 Manifest Attribute Description Premain- This attribute definesthe Java agent class that includes Class the premain method to be usedwhen the JVM initializes. Agent-Class This attribute defines the Javaagent class that includes the agentmain method to be used after the JVMinitializes. Boot-Class- This attribute specifies a list of paths to besearched by Path the bootstrap class loader. Can- This optional, Booleanattribute specifies whether the Redefine- agent can redefine classes,with a default value of ‘false.’ Classes Can- This optional, Booleanattribute specifies whether the Retransform- agent can retransformclasses, with a default value of Classes ‘false.’ Can-Set- Thisoptional, Boolean attribute specifies whether the Native- agent can setnative method prefix, with a default value Method- of ‘false.’ Prefix

When used, the Java agent can instrument the application via any or allof the following approaches:

-   -   Redefining or retransforming classes at runtime to change the        bodies of methods, the constant pool, and/or attributes.    -   Modifying the failure handling of methods to allow for retry.        This allows the Java agent to monitor the performance of the        application, apply security rules to the application, and the        like.

FIG. 6 illustrates an example simplified architecture for a multi-tenantagent, according to various embodiments. As shown, architecture 600 mayinclude a Java application 602, a core agent 604, and a plurality oftenants 606 a-606 n (e.g., a first through nth tenant). Duringoperation, core agent 604 may function to insert instrumentation 608into Java application 602 on behalf of tenants 606 a-606 n for anynumber of purposes. For example, in one embodiment, tenant 606 a may bean APM utility that monitors the performance of Java application 602,while tenant 606 n may be a Runtime Application Self Protection (RASP)utility that implements a number of security checks within Javaapplication 602.

In some embodiments, Java application 602 may be a Java 9+ applicationexecuted within the Java Platform Module System (JPMS). As would beappreciated, a key distinction in JPMS over prior versions of Java isthe support of ‘Java modules’ within an application, such as Javaapplication 602. In general, a Java module may include the followinginformation as part of a module descriptor:

-   -   A name that uniquely identifies the module.    -   A set of dependencies between that module and those on which it        depends.    -   A listing of the packages that it makes available to other        modules via export. Note that this must be done explicitly and        that a package is implicitly unavailable to other modules, by        default.    -   The services that are offered by the module.    -   The services that the module consumes.    -   The other modules that are allowed to use reflection with the        module.        In addition to the module descriptor, each Java module may        include any number of related packages (e.g., code) and,        potentially, other resources (e.g., images, XML, etc.), as well.

More specifically, a module descriptor for a Java module may utilize anyor all of the following directives:

-   -   exports—this directive specifies the packages of the module that        are accessible by other modules.    -   uses—this directive specifies which service(s) are used by the        module. In general, a service is an object for a class that        implements an interface or extends the abstract class specified        in this directive.    -   provides—this directive specifies that a module provides a        particular service (e.g., the interface or abstract class from        the uses directive), as well as the service provider class that        implements it.    -   opens—this directive specifies the package(s) of the module that        are accessible to other modules. Depending on its use, this        directive can be used to allow all packages in the module to be        accessed during runtime or used to limit runtime access by        specified modules to certain modules.

A key feature of Java modules is the ability to restrict access betweenmodules. Indeed, in Java version 8 and prior, the Reflection API couldbe used to access all classes in a package, including its privateclasses, regardless of the access specifier used. With Java modules,classes in packages within a module need to have permission to access aclass and to perform reflection on a class. This is done by a module“exporting” itself and certain packages to another module that “reads”that module and its exported packages. In addition, a module can “open”itself to another module, to allow reflection.

To better describe the techniques herein, the following terminology isused:

-   -   ByteCode Instrumentation (BCI)—dynamically modifying Java        classes for the purpose of instrumentation (e.g.,        instrumentation 608).    -   JMX MBeans—a managed Java object, similar to a JavaBeans        component, that follows the design patterns set forth in the JMX        specification. An MBean can represent a device, an application,        or any resource that needs to be managed.    -   JMX Attribute—Defines a metric and metric data type exposed by        the MBean.    -   Javassist—This is a popular BCI toolkit used to instrument        classes.    -   Boot Class(es)—Core Java classes loaded by the “Boot Class”        loader (Java bootstrap native loader).    -   Non-Boot Class(es)—Classes found about the Boot loader in        Extension, Application, or Web Application loaders    -   Handler—This is an intercepting class that contains a method to        call on entry into an instrumented method and method to call on        exit from an instrumented method.    -   Transform—The act of altering the class bytes before loading.

In general, tenants 606 a-606 n are specific functional modules thatshare core agent 604 with other tenants. In some embodiments, eachtenant 606 may have its own, isolated class loader designed such thattenants 606 a-606 n do not conflict with one another. In furtherembodiments, each tenant 606 may have direct access to core agent 604via the classes in core agent 604, which is the parent for the tenantclass loader. During use, each tenant of tenants 606 a-606 n may residein a specific “tenants” folder within core agent 604 and may beconfigured via .yaml files.

In various embodiments, core agent 604 may leverage the javaagentarchitecture built into Java and be configured via an agentConfig.yml orsimilar file. More specifically, core agent 604 may be divided intothree areas:

-   -   Boot—this resides in the boot loader used to load Java        application 602    -   Premain—this method resides in the application class loader and        launches core agent 604 to set up the Agent Loader.    -   Agent Loader—this is the loader for core agent 604 and prevents        conflicts with Java application 602. In addition, agent        libraries are isolated from one another.

As noted, core agent 604 may leverage handlers, to insertinstrumentation 608 into Java application 602. More specifically,handlers are instrumentation points to intercept or gain control of themethod entry or exit within Java application 602 and controlled viaconfiguration. For example, a handler may receive an object instance andall of its arguments within Java application 602, as well as the returnvalue on exit and any exceptions that may be raised. In someembodiments, the handlers may use Reflection on any classes that are notin the boot loader (a core Java class), since those classes are directlyaccessible via a class loader designation. In further embodiments, thehandlers can pass information via Thread Local and access other handlersin a same tenant of tenants 606 a-606 n (e.g., using an API call).Handlers also have the option of intercepting entry/exit events andcatch exceptions via configuration, as well as receive the objectinstance and, on exit, all arguments, the return value, and anyexceptions raised. Control over a single handler can be regulated ahandler file, e.g., handlername.properties.

In other words, core agent 604 may provide the following to tenants 606a-606 n:

-   -   Isolation from other tenants of tenants 606 a-606 n (via        Classloading).    -   Automatic JPMS compatibility—e.g., handling the complexities of        Java 9+, such as module permissions, naming, class        instrumentation, conflict resolution, removal, shutdown per        tenant 606 without affecting tenants 606 a-606 n, security        (e.g., the automatic handling of tenant permissions for Java        Security Manager), etc.    -   Automatic thread naming based on the name of a tenant 606 (e.g.,        to ID tenant threads in stack sampling).    -   Automatic monitoring of tenant instrumentation measuring        latency, CPU, etc. (and, if needed, to automatically remove the        instrumentation).    -   Implementation of Async handling callbacks.    -   Ability to instrument anonymous classes, such as Lambda's,        functions, inner classes, and the like.    -   Logging and log management.    -   Support for new technologies.    -   Context launch.

To do so, core agent 604 may include any or all of the followingcomponents, in various embodiments:

-   -   1. An easy to use JMX MBean/Server interface which can be used        to publish data created by a handler (e.g., metrics) as        attributes and make them accessible to JMX consoles, such as        JConsole.    -   2. A built-in, optional, ‘light’ HTTP Web Server which can be        used for diagnostics.

Special adaptors can also be used to enable any Java agent to be loadedinto core agent 604. For example, these adaptors may accept an unalteredjavaagent jar, unpack them, and launch them in the context of otherservices (e.g., offered by the MT agent adapter factor). In addition,core agent 604 may leverage an instrumentation toolkit such asJavassist, or the like, to perform the bytecode injection (BCI) ofinstrumentation 608 into Java application 602.

In the context of Java 9+, core agent 604 may be implemented in amodularized fashion and may use an ‘extension’ class to the premainbootstrap process that would discover all of the JVM modules, as well asthe modules for core agent 604. The boot and premain sections have“unnamed” modules, and the agent loader section may be put into its ownmodule called “AgentModule” or the like.

On bootstrap, a tweak is made to the “java.base” module that exports twopackages required to complete the bootstrap. Immediately after that, anyother required modules are loaded such as “java.sql,” “jdk.httpserver,”or the like. Note that these are not loaded by the JVM, by default.Then, the AgentModule of core agent 604 is created by loading all the.jars and packages in the Agent Loader of core agent 604, which wascreated in premain. In turn, TenantModule(s) are created for each tenant606 found by core agent 604.

To enable tenants 606 a-606 n to instrument Java application 602, coreagent 604 may perform the following:

-   -   Set the boot module (unnamed) to be able to read the AgentModule        and TenantModules.    -   Set the AgentModule and TenantModules to export to the boot        module (int boot Class Loader) Sets the premain module (unnamed)        to be able to read the AgentModule and TenantModules.    -   Set the AgentModule and Tenant Modules to export to the premain        module (e.g., in Application Class Loader).    -   Set ALL OTHER modules (e.g., non-agent) to export, to be read        and be open to the AgentModule and Tenant Modules. In addition,        ALL Tenant modules export to the Agent Module and also are open        to the Tenant Module.

After startup, ANY new modules are reported during the Class Review(from new transforms) and are also set to export, be read, and be opento the Agent Module.

Startup of core agent 604 can be achieved either as a standalone Javaagent, or via other pluggable adapters offered by the multi-tenantagent. For example, to launch core agent 604 as a standalone Java agent,the -javaagent switch can be added to the startup, similar to thefollowing:

-   -   java -javaagent:prod/lib/javaagent.jar=agentConfig.yml

As noted, handlers of core agent 604 receive control during entry, exit,or exception events of a method. An example of such a handler is asfollows:

MethodHandler will receive calls on initialization and method entry,method exit:

 public static interface MethodHandler  {  public void initProxy(StringlaunchFrom, String agentArgs, Instrumentation instHandle);  public voidhandlerEntry(Object inst, Object[ ] args, String className, Stringmethod, String signature, String id);  public void handlerExit(Objectreturn Val, Object inst,  Object[ ] args, String className, Stringmethod, String signature, String id);  }

Instrumentation 608 by core agent 604 can be achieved in a number ofways. For example, the following illustrates one potentialimplementation that can be added to agentConfig.yml for core agent 604:

agent-instrumentation-properties: preloadClasses: // preload classes onstartup noTransformClass: agent.Java,com.singularity,com.appdynamics  //don't transform these packages noTransformLoaderClassName: JavassistAgentClassLoader,com.singularity,com.appdynamics  // don'ttransform from these loaders noTransformThreadName: // don't transformif this thread name classReviewInterval: 10 // how often to review newclasses (in seconds) useClassDefinitionTransform: true // To instrumentin the “middle”  of the class definition (experimental)useAnonymousClassTransform: true // To instrument anonymous  classes(experimental) useInitialTransform: false // To instrument in theinitial class  transform (experimental) showJMX: false // showinstrumentation or other metrics in JMX

Likewise, a lightweight server (e.g., part of the JVM) can be loaded toprocess service requests for diagnostics or other reasons viaagentConfig.yml. For example:

agent-server-configuration: keystorePassword: javaagent keystoreFile:javaAgentServerKeystore.jks sslProtocol: TLSV1.XINONE username: admin //requests should use basic auth and specify this username password:********** // requests should use basic auth and specify this passwordport: 8000 // port to listen on authenticate: truelfalse // requiresusername and password (via basic authentication)

Creation of a tenant 606 can be achieved by creating a named Tenantfolder that includes the following subfolders:

-   -   config: this subfolder should include:        -   All handler property files (optional)        -   The tenant config file (called tenantConfig.yml) which            specifies the instrumentation information    -   lib: this folder should include the .jar files which should be        included in the Tenant Class Loader

For example, the following can be included in tenantConfig.yml toimplement instrumentation for the tenant 606 that overrides the globalguidance for that tenant:

tenant-specific-instrumentation-properties: preloadClasses: // preloadclasses on startup noTransformClass:agent.Java,com.singularity,com.appdynamics  // don't transform thesepackages noTransformLoaderClassName: JavassistAgentClassLoader,com.singularity,com.appdynamics  // don'ttransform from these loaders noTransformThreadName: // don't transformif this thread name

Similarly, tenantConfig.yml may also specify the instrumentationspecifics for that tenant 606 in terms of handlers. For example:

tenant-instrumentation: class: name[,name,name,etc.] method:method[,method,method,etc.] signature:signature[,signature,signature,etc.] handler: handlers.name interface:truelfalse // Use this if the Class/Method specified is abstract(Extended or  an interface) catch: true // use this to pass theException instead of return Val for method exit calls load: truelfalse// load handler on startup inactive: truelfalse // Whether or is activeentry: truelfalse // Instrument method entry for method exit: true/false// Instrument method exits for method entrycode: java code // Optionalcode to execute on entry (one can specify  $STANDARD$ to insert thestandard proxy handler calls) exitcode: java code // Optional code toexecute on exit (one can specify  $STANDARD$ to insert the standardproxy handler calls) condition: System property name // Apply ONLY ifthis property is true

When specifying a custom entrycode or exitcode that will take place ofthe default entry/exit calls, it must have the proper Java syntax. Morespecifically, ‘;’ must be used between Java coding lines. In addition,some of the Javaassist tokens can be used, which will be converted atruntime, such as the following tokens:

-   -   $0, $1, $2, . . . this and actual parameters    -   $args—An array of parameters. The type of $args is Object[].    -   $$—All actual parameters. For example, m($$) is equivalent to        m($1,$2,.)    -   $cflow(. . . )—cflow variable    -   $r—The result type. It is used in a cast expression.    -   $w—The wrapper type. It is used in a cast expression.    -   $_—The resulting value    -   $sig—An array of java.lang.Class objects representing the formal        parameter types.    -   $type—A java.lang.Class object representing the formal result        type.    -   $class—A java.lang.Class object representing the class currently        edited.

The standard method handler calls (e.g., the default entry/exit calls)are as follows:

-   -   public static void handlerEntry(Object inst, Object[] args,        String className, String method, String signature, String id,        String handlerName) {    -   public static void handlerExit(Object returnVal, Throwable t,        Object inst, Object[] args, String className, String method,        String signature, String id, String handlerName)

Any handler can also add a URL context, and will receive inboundmessages to the JavaAgentServerinterface implementation, to the serverusing code similar to the following:

-   -   JavaAgentServer server=JavaAgentServer.getInstance( )        server.addService(false, “/testhandler”, this);    -   A handler can also have a properties file        (handlername.properties) for configuration and must be located        in the Tenant folder. For example:

Example (TestHandlerMethodProxy.properties): test.monitor.interval=10000

While performance monitoring may help ensure that a given applicationserves its users within guaranteed, required, etc. performance bounds.So far, instrumentation that has been used for APM has been implementedunder the assumption that the given application has its own built-insecurity mechanisms. These security mechanisms, however, may at times beinadequate, sidestepped, or even taken advantage of. For example, onetype of act that an application may be not be configured to address aremalicious insider acts, including privileged user abuse (also known asinformation technology (IT) sabotage). Privileged user abuse may beextraordinarily harmful and costly (more than just monetarily) for anorganization.

According to the Computer Emergency Response Team™ (CERT), IT sabotageoccurs when current or former employees, contractors, vendors orbusiness partners intentionally exploit an authorized level of access toapplications, data, systems, or networks with the intention of harming aspecific individual or an organization. Notably, a trusted user withprivileged access to an application oftentimes may use the applicationwith native security mechanisms disabled within the application. Forinstance, this may be done by the trusted user disabling the securitymechanisms or by default (e.g., the application is preconfigured to haveno or fewer security mechanisms enabled due to the trusted user'sstatus). In other examples, privileged access that is conventionallygiven to trusted users may include access to hardware systems (e.g.,network routers, switches, firewalls, etc.), software systems (e.g.,applications, microservices, APIs, etc.), data, and any other type ofinformation system.

Typically, trusted users with privileged access are the most at risk forcommitting IT sabotage. Examples of IT sabotage vary widely, based on anactors knowledge and motivation, and may include: a disgruntled workerat a company with administrator rights to an application deletingthousands of user accounts shortly before he or she leaves the company;an IT administrator committing sabotage by changing router passwords anddisabling the server after quitting a building products distributor; arecently fired system administrator extorting his or her former employerby refusing to reveal administrator passwords; and a systemadministrator of a financial institution, having been fired without anynotice, using a remote access connection to shut down servers of thefinancial institution for numerous days.

Instrumenting Applications to Prevent Abuse by Privileged Users

The techniques herein introduce instrumenting applications to preventabuse by privileged users. In some aspects, stakeholders and/ororganizations that own, operate, manage, run, etc. a particularapplication are protected due to enforced step-up authentication by“peers” of a given privileged user, at a per transaction or a permicroservice level. It is contemplated that the enforced step-upauthentication may be enabled across various information technologyassets (e.g. for example, applications, databases, Kubernetes clusters,networks, etc.) of an organization, in some implementations, through amulti-factor authentication proxy agent. As such, re-engineering orapplications or other systems is generally not required. Particularly,an agent may be situated alongside an application, database, etc. andmanaged by a centralized controller. If a privileged user initiates atask, transaction, etc. at an asset (e.g., application, database, etc.)that a) has been previously identified as “risky” and b) has not beenauthorized and scheduled as an approved change (e.g., by IT servicemanagement (ITSM) of an organization), then approval for the task,transaction, etc. may be requested from peers of the privileged user(e.g., other administrators with a same level of access). In an example,SMS text or MMS messages may be sent, where the peers may approve ordeny the requested action, and, it is contemplated where the requestedaction is deemed malicious, privileged access may be immediatelyrevoked.

In one or more embodiments, the requested approval from peers of theprivileged users may be implemented using an expedited and improveddecision approval process. In particular, rather than using atraditional chain-of-approval, a pool of predominantly peer approvers isleveraged, wherein each individual approver is continually anddynamically rated according to the reliability of theirapproval-decisions. This rating may be done computationally or bypeer-ratings (or by even combinations thereof). The potential gravity ofthe request to be approved may be a factor in determining a compositescore that needs to be achieved in order to approve the decision. Forexample, actions that have severe potential consequences will require ahigher aggregate score in order to be approved, and vice-versa.Decisions with broad implications would thus typically require a quorumof reliable peer approvals to proceed.

With reference again to FIG. 6 , core agent 604, which may be fromtime-to-time be described as a multi-tenant agent, may be used to gatherapplication runtime telemetry to be consumed by multiple independentsystems (i.e. “tenants,” as the name of the agent itself implies), asdescribed herein above. Stated another way, core agent 604 may be a Javaagent that allows multiple tenants to share use of the agent forpurposes of instrumenting an application. Of note, the multi-tenantagent allows different technologies, such as APM, RASP, etc., to coexistand across different vendors. Additionally, core agent 604 may supportthe complexities of JPMS in Java 9+, to provide proper classloading andto oversee instrumentation missteps, removing the burdens associatedwith supporting a full Java agent across multiple vendors.

Because core agent 604 may be used to gather telemetry for multiplesystems (e.g., via a tenant of tenants 606 a-606 n), multipleapplication agents from different vendors are enabled to concurrentlyrun on a single application (through core agent 604), which maypotentially present a variety of risks (e.g., in terms of cybersecurity,abuse of user privileges, etc.). A given tenant of tenants 606 a-606 nmay be implemented using bytecode interception (BCI) to as to pauseexecution of an application program, for example, java application 602.BCI, in combination with logic and integrations with various policyenforcement systems that are external to the application program (e.g.,a multifactor authentication system, notification and/or approvalsystem, etc.), may be implemented to address the risk presented withcore agent 604. For example, this combination may be used for detectionand identification unauthorized data mining actions by authorized usersof an application (e.g., java application 602). After the detection andidentification, one or more policy actions may be enforced by a tenant,through core agent 604, in real time using mechanisms external to aruntime domain of the application (e.g., to halt a detected andidentified data mining operation).

Stated another one, a given tenant of tenants 606 a-606 n, incombination with core agent 604 that runs on top of java application 602(and without impeding or haring execution of java application 602) mayprevent class loading errors and/or module system errors from occurringwithin java application 602. To this end, a tenant performing thisfunction may be implemented with a handlerEntry function such as:

public void handlerEntry(Object inst, Object[ ] args, String className,String method, String signature, String id) { Object requestObject =args[0]; Object responseObject = args[1];authenticationStatus.set(true); String url = getFullURL(requestObject);mtAgentTenantAPI.log(“Received inbound request: ” + url); // Redirectcoming from DUO if (isDuoAuthenticatedResponse(url)) {mtAgentTenantAPI.log(“>>>>> Redirected Transaction coming from DUO. ..” + url); return; } RuleInfo = getRuleMatch(url); if (ruleInfo == null){ return; } String authenticatedUsername =getUserPrincipal(requestObject); if (authenticatedUsername == null) {authenticatedUsername = defaultUser; } UserRuleInfo =getUserForRule(authenticatedUsername, ruleInfo.name);userRuleInfo.currentInstance++; String currentAction =“ ”;

The tenant may additionally be configured to perform a)blocking/preventing of request actions, operations, etc., b) triggeringa multi-factor authentication, c) triggering a notification (e.g.,within an application or tenant) to be sent to other users (than onethat initiated an action, operation, etc.), d) triggering a textmessaged notification (SMS) to other users. These actions may beimplemented using, as examples:

// Block Action if (userRuleInfo.doBlock ∥(userRuleInfo.currentInstance >= ruleInfo.actionInstances[BLOCK_ACTION]&& ruleInfo.actionInstances[BLOCK_ACTION] > 0)) { currentAction =coreActions[BLOCK_ACTION]; mtAgentTenantAPI.log(“Blocking on instance:” + userRuleInfo.currentInstance); doDialog(responseObject,authenticatedUsername, url, “blockPopup.js”); } // Trigger an MFA Actionelse if (userRuleInfo.currentInstance ==ruleInfo.actionInstances[MFA_ACTION]) { currentAction =coreActions[MFA_ACTION]; mtAgentTenantAPI.log(“Sent MFA on instance: ” +userRuleInfo.currentInstance); doMFA(responseObject,authenticatedUsername, url); } // Trigger a NOTIFY else if(userRuleInfo.currentInstance ==ruleInfo.actionInstances[NOTIFY_ACTION]) { currentAction =coreActions[NOTIFY_ACTION]; mtAgentTenantAPI.log(“Sent Notification oninstance: ” + userRuleInfo.currentInstance); doDialog(responseObject,authenticatedUsername, url, “warningPopup.js”); // Trigger an SMSnotification } else if (userRuleInfo.currentInstance ==ruleInfo.actionInstances[SMS_ACTION]) { currentAction =coreActions[SMS_ACTION]; mtAgentTenantAPI.log(“Sent SMS on instance: ” +userRuleInfo.currentInstance); String subject = “Security Alert(violation rule: “ + ruleInfo.shortName + ”) for user ” +authenticatedUsername; if (ruleInfo.notifyList.size( ) > 0) { for(String notify : ruleInfo.notifyList) { sendMail(notify, subject,getDecisionContent(authenticatedUsername), “text/html”); } } else {mtAgentTenantAPI.logWarning(“No notification list for notifying. . .”);} } addCall(url, authenticatedUsername, userRuleInfo.currentInstance,currentAction);

FIGS. 7A-7E illustrate an example simplified architecture forinstrumenting applications to prevent abuse by privileged users.Notably, architecture 700, as shown in FIG. 7A, may comprise amulti-factor authentication proxy controller 702 that is incommunication with a plurality of application server agents 704, aplurality of sidecar proxy agents 706, and a plurality of network deviceagents 708. Generally, each of plurality of application server agents704 may execute core agent 604 that, as described herein, is enabled toidentify specific transactions within a server application, for example,a user related to a transaction, a role of the user, details of thetransaction, etc. In addition, core agent 604 may be used to enforcemulti-factor authentication (MFA) and/or continuous MFA (CMFA) forspecific transactions within the server application and any of theadditional actions, for example, blocking a transaction, notifying auser, etc., as described with respect to FIG. 6 . In one or moreembodiments, it is contemplated that the MFA or CMFA may be additionallyimplemented so as to provide specific contextual information that can beused in the determination and feeding into the one or more machinelearning and/or artificial modules described herein (e.g., a user'slocation at accessing, time of day at location, etc.).

In the context of cloud-native application architecture, functionalityof core agent 604 may be enabled by implementing a proxy, shown assidecar proxy agents 706 in FIG. 7A, which may be described fromtime-to-time as a multi-factor authentication proxy (MFAP). Accordingly,in such architectures, core agent 604 may at a microservice level beenabled to enforce MFA, block a transaction, etc. As a proxy, MFAP maybe further enabled to run on hardware platforms using as plurality ofnetwork device agents 708 (e.g., on a network switch or a router), so asto trigger MFA for specific action(s) or to grant users specific levelsof access. Notably, network device agents 708 may implemented one ormore function for core agent 604 as a containerized application on agiven network device. For instance, when a user attempts to a change an“enable password setting” which conventionally requires a highest levelof privileged access for network devices, changing the enable passwordrequires the highest level of access (level access on Cisco devices);such an action could be gated by the MFAP on the network device.

Multi-factor authentication proxy controller 702 may be an or incommunication via an API 712 for IT service management system 710, wheremulti-factor authentication proxy controller 702 is configured toperform send, receive, etc. a number of notifications from IT servicemanagement system 710. It is to be understood that, conventionally,there is more than a single administrator for a given type of IT system.For example, it is common practice in most IT departments to havemultiple administrators for applications, databases, systems, networks,etc. That is, it understood that in most IT systems, it would beseverely limiting to have only a single administrator because if he orshe were unavailable, then necessary administrative functions could notproceed until their return, which would thus carry an unnecessary impactto the general productivity of the organization (not to mention theadditional exposure of the organization to privileged user abuse).

Multi-factor authentication proxy controller 702 may be configured toreceive, store, etc. lists of authorized administrators for a given ITsystem (e.g. applications, databases, containers, network devices, etc.)as well as corresponding notification mechanisms of the authorizedadministrators (e.g., a mobile phone number for text messages).Additionally, multi-factor authentication proxy controller 702 may beconfigured (e.g., by an administrative team for a given IT system) tostore a list of specific transactions and/or microservices to be gated,monitored, etc. in architecture 700 by instrumenting applications toprevent abuse by privileged users. These transactions may includeprivileged user tasks such as: a) adding or deleting more than x numberof users within an IT system within a day; b) enabling or disablingspecific policies for the IT system (e.g., access policies, encryptionpolicies, etc.); c) making critical configuration changes to the ITsystem (e.g., architecture modifications, turning on/off functions,etc.); d) capturing information from the IT system; or e) changing oneor more passwords and/or security credentials to the IT system.

As shown in FIG. 7B., multi-factor authentication proxy controller 702may be configured to communicate the list of specific transactionsand/or microservices to be gated, monitored, etc. as messages 714 torespective MFAP agents executing on application server agents 704,sidecar proxy agents 706, and network device agents 708. As noted,multi-factor authentication proxy controller 702 may be integrated withIT service management system 710 (e.g., ServiceNow™) using API 712, suchthat multi-factor authentication proxy controller 702 is aware of anyapproved and scheduled actions (changes, modifications,additions/deletions, etc.), details of system(s), users, etc. involved,objects/targets of the actions, etc.

Generally, each of application server agents 704, sidecar proxy agents706, and network device agents 708 to monitor, using instrumentationdescribed herein above and based on obtained lists of transactions,whether a gated transaction or microservice is being initiated. As shownin FIG. 7C, if one is observed, a given agent is configured to interceptthe request and to send username and transaction information as messages716 to multi-factor authentication proxy controller 702. Examples ofsuch messages may include:

-   -   user1@ITsystemA.com is requesting to delete 100 user accounts        from application A;    -   user2@ITsystemA.com is requesting to delete Kubernetes cluster        B;    -   user1@ITsystemB.com is requesting to change the encryption        policy for microservice C from strict to permissive;    -   user2@ITsystemB.com is requesting to change the admin password        of network device D; and    -   user1@ITsystemD.com is requesting to change the access policy of        firewall E.        Upon obtaining one or more of messages 716, multi-factor        authentication proxy controller 702 may be configured to        determine whether an action indicated by a given message falls        within a list of approved changes, actions, etc. (e.g., obtained        from IT service management system 710). If the action is an        approved one, then the action may be permitted to proceed by        multi-factor authentication proxy controller 702.

If the action is an unapproved one, then multi-factor authenticationproxy controller 702 is configured to exchange notifications and/ormessages 718 to other administrators 720 within the IT system where theattempted action has taken place based on. As shown in FIG. 7D,multi-factor authentication proxy controller 702 may be configured toexchange messages 718 (e.g., via SMS) to the other authorizedadministrators in a same group of the privileged user that has attemptedan action, along with instructions of how to approve or deny the request(e.g., messages 718 may comprise a series of exchanges or checks). Forexample, a message via SMS may be “User user1@ITsystemA.com isrequesting to delete 100 user accounts from application A. Enter Y topermit or N to deny,” and authorized users that receive this message mayreply with an appropriate response. In another embodiment, thenotification may be sent to an application associated with multi-factorauthentication proxy controller 702 and/or IT service management system710. Messages 718 that are exchanged may, in addition to or in lieu ofSMS, may be based on any individual or combination of a securecommunications channel, an authenticated channel, an application in amobile device or browser, an SMS, MMS, a phone call, etc. For example,in an embodiment using MMS, a pre-agreed image may be sent to approvers,as understood by one having skill in the art. It is contemplated thatexchanging messages 718 may be an open exchange or pre-authenticated, soas allow multi-factor authentication proxy controller 702 to be“continuous.”

Furthermore, it is contemplated that prior to or as part of exchangingmessages 718 with other administrators 720, multi-factor authenticationproxy controller 702 may be configured to initiate an administrator toperform one or more multi-factor authentication (MFA) and/or continuousMFA procedures/protocols. Notably, such additional MFA requirement wouldensure and/or enhance trust in a given “vote” by a peer/authorizedadministrator. It is contemplated that the message described above mayinclude a URL to a particular MFA procedure/protocol that is required tobe completed for a vote to be considered in determining, by multi-factorauthentication proxy controller 702, whether an action is to beapproved. Additionally, more than one authentication check iscontemplated being required for a particular action to occur. That is, asingle authentication check or multiple authentication checks may dependon a) a context from API 712 or from a response in messages 718 orb)whether any of the peer/authorized administrators are online.

When the required number of authorized administrators have approved therequested action, then multi-factor authentication proxy controller 702is configured to permit a requested the MFAP to continue the action. Itis contemplated that a notification 722 (e.g., an SMS message) may besent to privileged user 724 who initiated the request, including thedetails of which of his/her peers approved the action. It iscontemplated that some actions may only require a single peeradministrator to approve, while other actions may require multiple peeradministrators to approve, due to the higher risk of the requestedaction to the organization. Further, as will be described further hereinbelow, for actions that require multiple peer administrator approval,such decisions may be based on one or more quorum settlement policiesthat multi-factor authentication proxy controller 702 is configured toapply (achieve timely, yet reliable, approval-decisions).

In situations where the requested action is not approved by a peeradministrator (or a sufficient percentage, number etc.) of peeradministrators, then multi-factor authentication proxy controller 702 isconfigured to instruct the MFAP to indefinitely intercept the requestedtransaction. Further, a corresponding SMS message, notification, etc.may be sent to a privileged user, presumably a rogue administrator, thatinitiated the action which has been denied. For example, such messagemay be: “You have requested to change to delete 100 user accounts fromapplication A; however, this has not been approved by your peeradministrators within the required time. The requested action will notbe permitted to proceed.” Such message may inform the requestedprivileged user why the requested action is not proceeding (e.g., due topolicy, due to a technical error, etc.).

Additionally, it is contemplated that a peer administrator may considera requested action to be abusive, indicative of malicious activity, etc.to the point that such action would generally require a responsiveaction. In such scenarios, multi-factor authentication proxy controller702 may be configured to send, in messages 718, an additional option(s).For example, one option may be to revoke privileges of the user,reporting the user's actions to security personnel, etc. Such option maybe, for example, “User user1@ITcompanyA.com is requesting to delete 100user accounts from application A. Enter Y to permit or N to deny. EnterXXYYZZ to temporarily revoke all privileged access for useruser1@ITcompanyA.com.”

Enhanced Composite Quorum Settlement

Generally, quorum-based voting systems have long been used intechnology. For example, quorum-based systems are often used whenmultiple, redundant technical elements exist within a system. Forexample, a three control point handling may be implemented for criticalfunctions such as disk writes, vehicle controls, etc., where a majorityof the systems must “vote” towards a common outcome for that outcome tobe realized and expressed. However, such quorum-based systems, as usedin technical settings to drive technology outcomes or decisions, aretypically relatively simple in that they are oftentimes binary decisions(e.g., does something work or not, is something approved or not, etc.).That is, these systems may lack nuanced characteristics that arerequired to be applied to larger technical approvals within anorganization when moving beyond relatively simple, point-focused tasks.One such scenario that may be determining whether a sufficient number orspecific combination of peer/authorized administrators approve aprivileged user's action within an IT system, as described above withrespect to FIGS. 7A-7D.

Accordingly, as shown in FIG. 7E, multi-factor authentication proxycontroller 702 may be configured to, optionally, make determinationsregarding whether a requested action of privileged user 724 is to beapproved, based on “votes” of other administrators 720, using enhancedcomposite quorum settlement service 726. Enhanced composite quorumsettlement service 726 provides robust, yet still streamlined approvalprocess. Further, while it is described with respect to determinationsof whether a number of administrators have approved an action, thetechniques herein are contemplated to be applicable to a broader rangeof approvals within organizations. Generally, an action is to beanalyzed for approval using enhanced composite quorum settlement service726 may be statically flagged (e.g., by being included in a list asdescribed herein above) may be dynamically flagged based on observedanalytics of user behavior.

Accordingly, multi-factor authentication proxy controller 702 may beconfigured by enhanced composite quorum settlement service 726 todynamically group one or more of the other administrators 720 toidentify a number of them as an approvers group. Of note, additionalapprovers may be dynamically added to an approver group for moresignificant, impactful, risky, etc. actions). Generally, these approversmay predominantly be peers of the requester (e.g., privileged user 724),and not necessarily members of requester organizational hierarchy. It iscontemplated that hierarchical managers may be involved and invoked aspart of the approver group, with variable weightings applied based onthe organizational hierarchy.

Members of a given approvers group do not necessarily have an equal votein the approval process. Rather, their votes are dynamically weightedbased on a variety of factors, which may be indicated by an ApproverReputation Score (ARS). ARS is a measure of how “reliable” an approveris, and it may be determined using a number of different methods,operating separately or in combination. For example, if an approveralways approves requests within a certain amount of time, like fiveminutes, of receiving them, this may be identified as indicating a lackof due diligence in evaluating the requests, thereby resulting in alower ARS. Conversely, an approver could be anonymously rated by otherapprovers in a group/team, and if noted by those approvers as someonethat is diligent and thoughtful, such ratings could result in a higherARS.

As an example, consider that there are three approvers that have beendynamically grouped together to approve a given action, as follows:

-   -   Approver-1 with an ARS of 80/100 (indicating a reliable        approver);    -   Approver-2 with an ARS of 60/100 (indicating a somewhat-reliable        approver); and    -   Approver-3 with an ARS of 30/100 (indicating an unreliable        approver).

A combined score that is required, by enhanced composite quorumsettlement service 726, to approve a given task may vary. For example,one task might require an approval score of 100 (or higher) to proceed.If, for example, Approvers 2 and 3 approved the action, but Approver 1did not, the resulting combined score (60+30) would be below thispre-set bar (of 100), and the action would be denied, even though twoout of three approvers voted for it. On the other hand, if Approvers 1and 2 voted for it, the action would be approved, as the combined score(80+60) would exceed the required score. In this specific case, nosingle vote would be enough to get over the pre-set bar, and as such aquorum for approval is forced. In another example, a combined score forthe action to be approved may be set at a lower level (such as 75), sothat the action potentially could be approved by a single approval. Arequirement may be configured such that the single approver must beidentified as a reliable approver, otherwise, a quorum may still berequired.

A required quorum may also be adjusted so as to provide flexibility, asin the case of an approval chain that leads to a single individual whomay be otherwise occupied and not available to make a rapid decision toapprove a request (e.g., a vice president, tech lead, etc.). In such acase, by leveraging a pool of multiple approvers who are at peers of therequester (and as such each of these has a working knowledge of thedecision and its potential ramifications), decision making can beexpedited without sacrificing the overall quality of the decision makingprocess. Furthermore, to guard against multiple less-reliable approverscolluding to drive an approval into an approved state, a lower boundcould be dynamically set (based on the severity or consequences of theaction involved), so that the decision would only be directed toapprovers with a minimum reputation score (e.g. 40 or higher). In thismanner, more serious actions would only be directed to approvers whohave demonstrated themselves reliable.

In one or more embodiments, based on a given action involved, a level ofapproval required may be dynamically derived by enhanced compositequorum settlement service 726. For example, some relatively minor orlocalized actions (e.g., deleting one or two user accounts, changing asingle end-user password, etc.) might receive approval with an ARS scoreof only 60 (rather than 100), such that in the above example such aminor or localized action could be approved by either approver 1 alone,or approvers 2 and 3 acting in concert. However, for actions that have awider impact (e.g., deleting many user accounts, changing one or morepasswords and/or security credentials on critical systems), an approvalscore or 120 (or even higher) might be required, meaning that multiplemore-trusted approvers would have to act in concert to approve such anaction.

As noted above, privileged user abuse may be extraordinarily harmful andcostly for an organization. Related to privileged user abuse is datamining, a type of an abusive act that is oftentimes attributable tooriginating from within an organization. Notably, a recent analysis ofinformation security breaches has found that approximately 23 percent ofattacks may be categorized as “internal incidences”, where information,data, etc. is leaked from within an organization using user accounts. Ontop of this, when combined with third-party attacks/incidents, almosthalf of all information security breaches originate from authorized useraccounts. Unsurprisingly then, internal threats, which include datamining and leaking (by employees and third-parties), is a top-of-mindsecurity issue.

Mitigating and/or preventing information security breaches by authorizedusers and/or user accounts, however, presents numerous challenges,including:

-   -   authorized users, due to their role, function, etc. in        organizations, are given user accounts as well as access in        performing their duties, daily tasks, etc.;    -   differentiating legitimate and productive access to secure        information from harmful and potentially malicious access to        secure information (i.e., data mining) is difficult to        determine;    -   correlating logs from various sources conventionally requires        significant amounts of time, during which significant data can        be mined and leaked if such behavior continues unabated, to        yield actionable insights; and    -   implementation of client-side software, as one option to        mitigate data mining and exfiltration, is not a solution that        every organization is willing and able to mandate and enforce.

Detecting Anomalous Transactions Within an Application by PrivilegedUser Accounts

The techniques herein, therefore, introduce detecting anomaloustransactions within an application by privileged user accounts. In someaspects, inferences are made regarding monitored activity associatedwith user accounts of the online application, where the inferences areindicative of data mining and/or exfiltration activities. The inferencesmay be made without requiring client-side application(s) to beinstalled, distributed, managed, and enforced alongside the onlineapplication. Notably, the user accounts, while typically intended foruse by only by authorized users, may be taken advantage of by internalemployees, authorized third parties, or even people that have co-optedthe user accounts.

That is, a standalone application agent and/or tenant (of a multi-tenantagent) may be leveraged along with machine learning techniques toidentify, intercept, and block potential data mining activity associatedwith user accounts of a given online application (from within the onlineapplication's runtime domain). The machine learning techniques generallyencompass establishing a baseline behavioral model based on previoustransactions of the online application monitored through instrumentationtechniques as described herein above. For example, for a particular useraccount or user accounts generally, expected behavior such as a useraccount opening a number of records/files/etc. in a short amount oftime, viewing a given record/file/etc. for minimum (or maximum) amountof time, etc. may be determined. Subsequent transactions by useraccounts of the online application may then be compared to a behaviormodel, and ones that do not fall within expected behavior are identifiedas suspicious and/or anomalous. These identified transactions may thenbe notified, reported, etc. to IT system administrators (who maydetermine one or more mitigation actions to be taken). Alternatively,one or more enforcement policies may be applied for the identifiedtransactions that mitigate the identified transactions. For example,instrumentation inserted by the application agent/tenant into the onlineapplication may be used to block an identified suspicious transaction,without the need of re-engineering application code (of the onlineapplication) or having to install client-side software.

Specifically, according to one or more embodiments described herein, adevice obtains data regarding a transaction attempted by a user accountwithin an online application that is captured by instrumentation codethat is inserted into the online application at runtime, wherein theuser account has sufficient privileges within the online application toperform the transaction. The device makes an inference about the dataregarding the transaction using a behavioral model. The devicedetermines, based on the inference, a mitigation action for performancewithin the online application according to an enforcement policy. Thedevice enforces the mitigation action within the online application.

Operationally, with reference to FIG. 8 , an example simplifiedagent/tenant for detecting anomalous transactions within an applicationby privileged user accounts is shown. Agent/tenant 800 may comprise,perform the functions of, etc. of previously described plurality oftenants 606 a-606 n and/or plurality of application server agents 704.Agent/tenant 800 may be configured to monitor an application (e.g., javaapplication 602) such as a confidential document system, database, etc.that is generally configured to provide access to records to useraccounts (that have varying levels of privileged access to the records)and is at risk of being data mined by users that have access to theseuser accounts. Further, the application may be running within anenterprise computing environment. Records may comprise confidentialdocuments that may only be accessed by some user accounts, modified byanother set of user accounts, etc., as is understood by one having skillin the art. Generally, records are deemed confidential and/or onlyaccessible by particular user accounts (of the online application) byadministrators or stakeholders of the online application itself, withouthaving to rely on policing metadata insertion on a record-by-recordbasis, as is conventionally done. Also, records may comprise a varietyof file types and are not bound by or limited to a particular file type(or file types) like .pdf, .docx, etc.

It is contemplated that a user using the application, with particularuser account, may typically review records within the application byreading, digesting, evaluating, interacting, making comments, providingfeedback, approving/denying, etc. for a given record. Generally, each ofthese actions/transactions may be tracked, monitored, and reported (interms of which user account is accessing it, for how long, from where,etc.) using instrumentation techniques of core agent 604, as describedherein above.

Agent/tenant 800 comprises transaction monitor component 802, behavioralmodel component 804, policy engine 806, and enforcement engine 808,which may be implemented for detecting anomalous transactions within anapplication by privileged user accounts. Transaction monitor component802 is configured to obtain information indicative oftransactions/actions that occur within a given online application at aperiodic basis (e.g., one a minute). In particular, such information cangenerally summarize actions/transactions that occur with respect torecords available on a confidential document system, specifically withrespect to user accounts (that are given access to these records). Forexample, core agent 604 may be configured to intercept, summarize, andexport transaction details for java application 602 to transactionmonitor component 802, where the transaction details may be parsed byuser identifier(s)/name(s), transaction identifier(s)/name(s) (e.g.,accessing a record, modifying a record, etc.), time taken for a giventransaction, etc. Generally, as described herein, it is contemplatedthat core agent 604 may have the ability to allow or disallow a giventransaction to proceed based on instructions that it receives fromagent/tenant 800. In one or more embodiments, agent/tenant 800 mayreceive the information over a data pipeline such as a Cloud NativeKafka solution.

Behavioral model component 804 may, using the transaction detailsobtained by transaction monitor component 802, be configured to applyone or more machine learning techniques to model, predict, profile, etc.normal and abnormal behavior associated with user accounts of the onlineapplication (e.g., confidential document system). This may be done atmultiple levels of abstraction, including profiling at:

-   -   an application level, defining normal and anomalous behavior for        user accounts as a whole;    -   a role level, defining normal and anomalous behavior for a given        subset/grouping of the user accounts (e.g., user accounts denote        as reviewers); and    -   an individual user level, defining normal and anomalous behavior        for a particular user account of the online application (e.g.,        userABC).        Results of the machine learning-based profiling done by        behavioral model component 804 may be summarized. Example        observations that may be derived, at a role level for a given        online application, in summary, may include:    -   95% of the time, reviewer-level user accounts open a maximum of        ten records per hour;    -   15 95% of the time reviewer-level user accounts spend a minimum        of five minutes on each individual record;    -   95% of the time reviewer-level user accounts spend a maximum of        30 minutes on each individual record; and    -   95% of the time reviewer-level user accounts do not open more        than three sequentially-numbered records in a row.        It is contemplated that behavioral model component 804 may        comprise an inference engine to derive insights into potentially        suspicious abnormal behaviors based on these aforementioned        observations. Further, in one or more embodiments, it is        contemplated that the MFA or CMFA may be leveraged and        implemented so as to provide specific contextual information        that can be used in the determination and feeding into the one        or more machine learning and/or artificial modules of behavioral        model component 804 (e.g., a user's location at accessing, time        of day at location, etc.).

Policy engine 806, in combination with behavioral model component 804,may be configured to present an administrator (e.g., of an IT systemassociated with the online application/confidential document system) oneor more insights regarding user account behavior, as previouslydescribed. Notably, the administrator may decide when and how policiesare to be implemented to mitigate user actions indicative ofdata-mining. For example, these insights may include identifyingbehaviors that may be so unusual that they may be potentially seen assuspicious or likely indicating data mining, such as:

-   -   it may be determined that it is highly anomalous and potentially        suspicious behavior for time reviewer-level user accounts to        rapidly open multiple records of an online application in a row,        while spending only a few seconds on each (i.e., opening a        record for less than five seconds, one after another, is        generally understood as being long enough to print/save/copy the        information but not long enough to read and digest it); and    -   it may be determined that it highly anomalous and potentially        suspicious behavior for time reviewer-level user accounts to        open several sequentially-numbered records in a row, for        example, record #8001, then #8002, then #8003, then #8004, etc.        (i.e., for a given online application it may be rare for a        reviewer to sequentially select one record number after the next        given how records are distributed among reviewers).        For each of these insights, policy engine 806 may present an        administrator with enabling a specific policy (e.g., mitigation        action(s)) or dismissing an insight. For example, a given        insight may have one or more of the following policies selected,        including an alerting policy (to inform key stakeholders of        potential suspicious actions), an interception policy (that        pauses the requested transaction from completing until        appropriate approval is received), a blocking policy that blocks        transactions that fall within an insight, etc.

It is contemplated that a given insight may have more than oneassociated policy implemented for it. That is, an administrator mayconfigure an insight to have more than one threshold, where eachthreshold is associated with a particular mitigation action. As shown inFIG. 9 , an example insight and associated enforcement policy are shown.Notably, chart 900 is indicative of observed occurrences regarding therapid opening of multiple records for a given online application. Forthis insight, an administrator may configure a first threshold 902 thatcauses an alert and observe policy to be implemented for transactionsthat are indicative of a user account being used to open a thresholdnumber of records for less than ten seconds in a row. A second threshold904 may additionally be applied that causes an intercept until approvedpolicy to be implemented for transactions that are indicative of a useraccount being used to open another threshold number of records for lessthan ten seconds in a row.

Returning to FIG. 8 , enforcement engine 808 may, in combination withcore agent 604, be configured to implement and enforce policiesassociated with insights (provided by behavioral model component 804 andpolicy engine 806). That is, when a policy event threshold is observedto have been reached (e.g., by agent/tenant 800) enforcement engine 808is configured to implement a given enforcement policy. In the case ofalert and observe policy, the policy mitigation action may simply be tosend a message or notification to relevant stakeholders (e.g., a directmanager associated with a user account, peers of the user account,etc.), in accordance with the techniques described herein above. Such anotification may be sent by email, SMS, online chat application, anyother notification system/application. For example, the notification maycomprise a SMS or MMS message indicating “ALERT: username1 has beenrapidly opening an anomalous number of records; such actions may beindicative of data-mining. THIS IS AN ALERT ONLY. NO ACTION IS REQUIREDAT THIS TIME.”

In the case of intercept until approved policy, the policy mitigationaction may be to indefinitely intercept a requested transaction untilexplicit approval is obtained, also in accordance with the techniquesdescribed herein above (e.g., using a quorum settlement policy).Additionally, requests for approval may similarly be sent by the samenotification systems and/or applications as the alert and observepolicy. For example, the notification may comprise a SMS or MMS messageindicating “Rule Violation: username1 has been rapidly opening anexcessive number of records; such actions may be indicative ofdata-mining. Please choose an action to be taken: ALLOW, BLOCK,TEMPORARILY REVOKE.”

It is contemplated that additional policy mitigation actions may also besupported. One may be to temporarily remove user account access to anonline application (e.g., for a predetermined amount of time, until aproper investigation may be completed, etc.). Alternatively, another onemay be effectively “quarantine” a device associated with a user accountsuch that the device may no longer access an online application.Further, it is generally contemplated that in all cases where a policymitigation action is performed, such events may be exported to user andentity behavior analytics (UEBA) systems, such as Exabeam™ for thepurposes if sharing information and/or insights. Event logs may also beexported to security incident response systems, such as Cisco SecureX™to properly report, investigate, and (when necessary) prosecuteincidents.

In closing, FIG. 10 illustrates an example simplified procedure fordetecting anomalous transactions within an application by privilegeduser accounts, in accordance with one or more embodiments describedherein. For example, a non-generic, specifically configured device(e.g., device 200, particularly a monitoring device) may performprocedure 1000 by executing stored instructions (e.g., process 248, suchas a monitoring process) that include a multi-tenant agent to instrumenta Java application. The procedure 1000 may start at step 1005, andcontinues to step 1010, where, as described in greater detail above, thedevice may obtain data regarding a transaction attempted by a useraccount within an online application that is captured by instrumentationcode that is inserted into the online application at runtime, whereinthe user account has sufficient privileges within the online applicationto perform the transaction. In one embodiment, the data regarding thetransaction attempted by the user account within the online applicationmay comprise at least one of a user identifier, a username, atransaction identifier, a transaction name, and a time taken for thetransaction. In one or more embodiments, the instrumentation code may beinserted by a core agent into the online application.

At step 1015, as detailed above, the device may make an inference aboutthe data regarding the transaction using a behavioral model. In oneembodiment, the behavioral model may be generated based on transactionspreviously monitored by the instrumentation code or contextualinformation from a continuous multi-factor authentication. In one ormore embodiments, making, by the device, the inference about the dataregarding the transaction using the behavioral model may be based on arole level associated with the user account. As would be appreciated,the inference may be that the user account is being used rapidly open aplurality of records of the online application in a row while viewingeach of the plurality of records less than a predetermined amount oftime. In an embodiment, the inference may be that the user account isbeing used to open a predetermined number of sequentially numberedrecords of the online application.

At step 1020, the device may determine, based on the inference, amitigation action for performance within the online applicationaccording to an enforcement policy. In one or more embodiments, themitigation action for performance within the online applicationaccording to the enforcement policy may comprise a plurality ofthresholds that are each associated with a corresponding mitigationaction.

At step 1025, as detailed above, the device may enforce the mitigationaction within the online application. In one or more embodiments, themitigation action for performance within the online applicationaccording to the enforcement policy may comprise an alert and observepolicy, for example, sending a SMS or MMS message to an administrator ofthe online application. In an embodiment, the mitigation action forperformance within the online application according to the enforcementpolicy may comprises an intercept the transaction until approved policy,where the transaction may be blocked if not approved.

The procedure 1000 may then end in step 1030, notably with the abilityto continue ingesting and processing data. Other steps may also beincluded generally within procedure 1000.

It should be noted that while certain steps within procedure 1000 may beoptional as described above, the steps shown in FIG. 10 are merelyexamples for illustration, and certain other steps may be included orexcluded as desired. Further, while a particular order of the steps isshown, this ordering is merely illustrative, and any suitablearrangement of the steps may be utilized without departing from thescope of the embodiments herein.

The techniques described herein, therefore, provide detecting anomaloustransactions within an application by privileged user accounts. Inparticular, inferences are made regarding monitored activity associatedwith user accounts of the online application (e.g., usinginstrumentation of core agent 604), where the inferences are indicativeof data mining and/or exfiltration activities. Potential data miningactivity associated with user accounts of a given online application(from within the online application's runtime domain) may then beidentified, intercepted, and blocked. By deploying this method, theinstrumentation inserted by the application agent/tenant into the onlineapplication may also be used to block an identified suspicioustransaction, without the need of re-engineering application code (of theonline application) or having to install client-side software.

Illustratively, the techniques described herein may be performed byhardware, software, and/or firmware, such as in accordance with theillustrative application monitoring process 248, or another Java agent,which may include computer executable instructions executed by theprocessor 220 to perform functions relating to the techniques describedherein, e.g., in conjunction with corresponding processes of otherdevices in the computer network as described herein (e.g., on networkagents, controllers, computing devices, servers, etc.).

According to the embodiments herein, a method herein may comprise:obtaining, by a device, data regarding a transaction attempted by a useraccount within an online application that is captured by instrumentationcode that is inserted into the online application at runtime, whereinthe user account has sufficient privileges within the online applicationto perform the transaction; making, by the device, an inference aboutthe data regarding the transaction using a behavioral model;determining, by the device and based on the inference, a mitigationaction for performance within the online application according to anenforcement policy; and enforcing, by the device, the mitigation actionwithin the online application.

In one embodiment, the data regarding the transaction attempted by theuser account within the online application comprises at least one of auser identifier, a username, a transaction identifier, a transactionname, and a time taken for the transaction. In another embodiment, thebehavioral model is generated based on transactions previously monitoredby the instrumentation code or contextual information from a continuousmulti-factor authentication. In a further embodiment, making, by thedevice, the inference about the data regarding the transaction using thebehavioral model is based on a role level associated with the useraccount. In yet another embodiment, the inference is that the useraccount is being used rapidly open a plurality of records of the onlineapplication in a row while viewing each of the plurality of records lessthan a predetermined amount of time. In another embodiment, theinference is that the user account is being used to open a predeterminednumber of sequentially numbered records of the online application. In anadditional embodiment, the mitigation action for performance within theonline application according to the enforcement policy comprises analert and observe policy. In a further embodiment, the mitigation actionfor performance within the online application according to theenforcement policy comprises an intercept the transaction until approvedpolicy. In yet another embodiment, the mitigation action for performancewithin the online application according to the enforcement policycomprises a plurality of thresholds that are each associated with acorresponding mitigation action. In another embodiment wherein theinstrumentation code is inserted by a core agent into the onlineapplication.

According to the embodiments herein, a tangible, non-transitory,computer-readable medium herein may have computer-executableinstructions stored thereon that, when executed by a processor on adevice, may cause the device to perform a method comprising: obtaining,by the device, data regarding a transaction attempted by a user accountwithin an online application that is captured by instrumentation codethat is inserted into the online application at runtime, wherein theuser account has sufficient privileges within the online application toperform the transaction; making, by the device, an inference about thedata regarding the transaction using a behavioral model; determining, bythe device and based on the inference, a mitigation action forperformance within the online application according to an enforcementpolicy; and enforcing, by the device, the mitigation action within theonline application.

Further, according to the embodiments herein an apparatus herein maycomprise: one or more network interfaces to communicate with a network;a processor coupled to the network interfaces and configured to executeone or more processes; and a memory configured to store a processexecutable by the processor, the process, when executed, configured to:obtain data regarding a transaction attempted by a user account withinan online application that is captured by instrumentation code that isinserted into the online application at runtime, wherein the useraccount has sufficient privileges within the online application toperform the transaction; make an inference about the data regarding thetransaction using a behavioral model; determine, based on the inference,a mitigation action for performance within the online applicationaccording to an enforcement policy; and enforce the mitigation actionwithin the online application.

While there have been shown and described illustrative embodimentsabove, it is to be understood that various other adaptations andmodifications may be made within the scope of the embodiments herein.For example, while certain embodiments are described herein with respectto certain types of networks in particular, the techniques are notlimited as such and may be used with any computer network, generally, inother embodiments. Moreover, while specific technologies, protocols, andassociated devices have been shown, such as Java, TCP, IP, and so on,other suitable technologies, protocols, and associated devices may beused in accordance with the techniques described above. In addition,while certain devices are shown, and with certain functionality beingperformed on certain devices, other suitable devices and processlocations may be used, accordingly. That is, the embodiments have beenshown and described herein with relation to specific networkconfigurations (orientations, topologies, protocols, terminology,processing locations, etc.). However, the embodiments in their broadersense are not as limited, and may, in fact, be used with other types ofnetworks, protocols, and configurations.

Moreover, while the present disclosure contains many other specifics,these should not be construed as limitations on the scope of anyembodiment or of what may be claimed, but rather as descriptions offeatures that may be specific to particular embodiments of particularembodiments. Certain features that are described in this document in thecontext of separate embodiments can also be implemented in combinationin a single embodiment. Conversely, various features that are describedin the context of a single embodiment can also be implemented inmultiple embodiments separately or in any suitable sub-combination.Further, although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asub-combination or variation of a sub-combination.

For instance, while certain aspects of the present disclosure aredescribed in terms of being performed “by a tenant”, “by a server”, or“by a controller,” those skilled in the art will appreciate that agentsof the application intelligence platform (e.g., application agents,network agents, language agents, etc.) may be considered to beextensions of the server (or controller) operation, and as such, anyprocess step performed “by a server” need not be limited to localprocessing on a specific server device, unless otherwise specificallynoted as such. Furthermore, while certain aspects are described as beingperformed “by an agent”, by particular types of agents (e.g.,application agents, network agents, etc.), or “by a tenant”, thetechniques may be generally applied to any suitable software/hardwareconfiguration (libraries, modules, etc.) as part of an apparatus orotherwise.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. Moreover, the separation of various system components in theembodiments described in the present disclosure should not be understoodas requiring such separation in all embodiments.

The foregoing description has been directed to specific embodiments. Itwill be apparent, however, that other variations and modifications maybe made to the described embodiments, with the attainment of some or allof their advantages. For instance, it is expressly contemplated that thecomponents and/or elements described herein can be implemented assoftware being stored on a tangible (non-transitory) computer-readablemedium (e.g., disks/CDs/RAM/EEPROM/etc.) having program instructionsexecuting on a computer, hardware, firmware, or a combination thereof.Accordingly, this description is to be taken only by way of example andnot to otherwise limit the scope of the embodiments herein. Therefore,it is the object of the appended claims to cover all such variations andmodifications as come within the true intent and scope of theembodiments herein.

What is claimed is:
 1. A method, comprising: obtaining, by a device,data regarding a transaction attempted by a user account within anonline application that is captured by instrumentation code that isinserted into the online application at runtime, wherein the useraccount has sufficient privileges within the online application toperform the transaction; making, by the device, an inference about thedata regarding the transaction using a behavioral model; determining, bythe device and based on the inference, a mitigation action forperformance within the online application according to an enforcementpolicy; and enforcing, by the device, the mitigation action within theonline application.
 2. The method as in claim 1, wherein the dataregarding the transaction attempted by the user account within theonline application comprises at least one of a user identifier, ausername, a transaction identifier, a transaction name, and a time takenfor the transaction.
 3. The method as in claim 1, wherein the behavioralmodel is generated based on transactions previously monitored by theinstrumentation code or contextual information from a continuousmulti-factor authentication.
 4. The method as in claim 1, whereinmaking, by the device, the inference about the data regarding thetransaction using the behavioral model is based on a role levelassociated with the user account.
 5. The method as in claim 1, whereinthe inference is that the user account is being used rapidly open aplurality of records of the online application in a row while viewingeach of the plurality of records less than a predetermined amount oftime.
 6. The method as in claim 1, wherein the inference is that theuser account is being used to open a predetermined number ofsequentially numbered records of the online application.
 7. The methodas in claim 1, wherein the mitigation action for performance within theonline application according to the enforcement policy comprises analert and observe policy.
 8. The method as in claim 1, wherein themitigation action for performance within the online applicationaccording to the enforcement policy comprises an intercept thetransaction until approved policy.
 9. The method as in claim 1, whereinthe mitigation action for performance within the online applicationaccording to the enforcement policy comprises a plurality of thresholdsthat are each associated with a corresponding mitigation action.
 10. Themethod as in claim 1, wherein the instrumentation code is inserted by acore agent into the online application.
 11. A tangible, non-transitory,computer-readable medium having computer-executable instructions storedthereon that, when executed by a processor on a device, cause the deviceto perform a method comprising: obtaining, by a device, data regarding atransaction attempted by a user account within an online applicationthat is captured by instrumentation code that is inserted into theonline application at runtime, wherein the user account has sufficientprivileges within the online application to perform the transaction;making, by the device, an inference about the data regarding thetransaction using a behavioral model; determining, by the device andbased on the inference, a mitigation action for performance within theonline application according to an enforcement policy; and enforcing, bythe device, the mitigation action within the online application.
 12. Thetangible, non-transitory, computer-readable medium as in claim 11,wherein the data regarding the transaction attempted by the user accountwithin the online application comprises at least one of a useridentifier, a username, a transaction identifier, a transaction name,and a time taken for the transaction.
 13. The tangible, non-transitory,computer-readable medium as in claim 11, wherein the behavioral model isgenerated based on transactions previously monitored by theinstrumentation code or contextual information from a continuousmulti-factor authentication.
 14. The tangible, non-transitory,computer-readable medium as in claim 11, wherein making, by the device,the inference about the data regarding the transaction using thebehavioral model is based on a role level associated with the useraccount.
 15. The tangible, non-transitory, computer-readable medium asin claim 11, wherein the inference is that the user account is beingused rapidly open a plurality of records of the online application in arow while viewing each of the plurality of records less than apredetermined amount of time.
 16. The tangible, non-transitory,computer-readable medium as in claim 11, wherein the inference is thatthe user account is being used to open a predetermined number ofsequentially numbered records of the online application.
 17. Thetangible, non-transitory, computer-readable medium as in claim 11,wherein the mitigation action for performance within the onlineapplication according to the enforcement policy comprises an alert andobserve policy.
 18. The tangible, non-transitory, computer-readablemedium as in claim 11, wherein the mitigation action for performancewithin the online application according to the enforcement policycomprises an intercept the transaction until approved policy.
 19. Thetangible, non-transitory, computer-readable medium as in claim 11,wherein the mitigation action for performance within the onlineapplication according to the enforcement policy comprises a plurality ofthresholds that are each associated with a corresponding mitigationaction.
 20. An apparatus, comprising: one or more network interfaces tocommunicate with a network; a processor coupled to the one or morenetwork interfaces and configured to execute one or more processes; anda memory configured to store a process that is executable by theprocessor, the process, when executed, configured to: obtain dataregarding a transaction attempted by a user account within an onlineapplication that is captured by instrumentation code that is insertedinto the online application at runtime, wherein the user account hassufficient privileges within the online application to perform thetransaction; make an inference about the data regarding the transactionusing a behavioral model; determine, based on the inference, amitigation action for performance within the online applicationaccording to an enforcement policy; and enforce the mitigation actionwithin the online application.